Program Director/Principal Investigator (Last, First, Middle):
Wong, Stephen, T.C., Ph.D., P.E.
Center for Systematic Modeling of Cancer Development
Section N1. Center Overview and Effort Integration
This section provides an overview of the proposed center, Center for Systematic Modeling of Cancer
Development (CSMCaD), including scientific focus, description of individual components and their integration,
as well as an estimated timeline for the overall program. The synergies to be achieved through the establishment
of multi-disciplinary teams and novel collaborations are fully described. The Center will draw its strength from an
inter-disciplinary, multi-institutional team of experienced investigators and a rich variety of laboratory and
institutional resources. The Center PI (Dr. Stephen Wong) and project lead investigators (Dr. Michael Lewis, Dr.
Jeffrey Rosen, Dr. Xiaobo Zhou, and Dr. Vittorio Cristini) will be responsible for developing and managing the
project such that we will have a representative decision-making process and administrative structure that will
allow resources to be allocated as needed to meet the scientific goals in a timely and cost-effective fashion.
N1.1. Overview of the scientific focus of the proposed Center (Abstract)
Excluding cancers of the skin, breast cancer is the most common cancer diagnosed in American women (1 in
8 women; about 13%) and is the second leading cause of cancer deaths among women. Systemic therapies
such as chemo- or radiation therapy are effective initially in controlling and reversing tumor growth. However,
residual cancers will invariably re-grow despite this initial response. While there have been several advances in
the treatment of breast cancer in the last two decades, notably targeted therapy for breast cancers expressing
estrogen receptor (ER+) or the HER2 (ErbB2) oncogene, breast cancer survivorship has improved only
modestly. Unfortunately, for women with “triple negative” breast cancers (lacking expression of ER,
progesterone receptor (PR) and HER2) we currently have no targeted therapies.
Our recent clinical data, as well as experimental evidence in both mouse mammary tumors and human
xenograft models, support the existence of a subpopulation of cancer cells present in the original tumor that are
greatly enriched in residual cancers after conventional systemic therapies. These residual cancer cells are
characterized by their intrinsic resistance to chemotherapy and relative growth quiescence. However, a discreet
subset of these residual cells possesses enhanced self-renewal capacity, as well as the ability to form tumors
upon transplantation. These residual tumor-initiating cells (TIC) (a.k.a. cancer stem cells (CSC)), which may be
located in certain tumor microenvironment (mE), may therefore be responsible for tumor growth, maintenance,
resistance to treatment, and disease relapse. If the hypothesis is correct, the failure of traditional systemic
therapies, such as radiation and chemotherapy, to cure breast cancer may be due to the fact that they incorrectly
target the highly proliferative cells, while allowing survival of treatment-refractory tumor-initiating “cancer stem
cells”. These findings fundamentally modify our conceptual approach to oncogenesis and have dramatic
implications for breast cancer prevention, treatment, and drug development.
In this proposal, we seek to build upon, and significantly extend, ongoing laboratory and clinical studies and
Figure 1. The flowchart for the proposed research for the Center for Systematic Modeling of Cancer Development
(CSMCaD)
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use newly developed experimental and imaging methodologies to identify, localize, purify, and characterize TIC
to a degree not possible before. This will then allow us to identify and image TIC in vivo and to model TIC
behavior during tumor development mathematically with respect not only to spatial localization and movement,
but also to proliferation, apoptosis, and specific changes in gene expression and cellular signaling. Combined
functional genomics and data mining strategies will allow us to characterize novel growth regulators.
Furthermore, our combined experimental and systems biology approach will allow us to evaluate responses to
experimental therapeutics that may inhibit or kill TIC specifically in a manner not possible before. Aside from a
wealth of basic biological insight, extensions of this work may allow drug repositioning as well as development of
directed, mechanism-based and “stem cell”-centric drug screening and evaluation methods.
Figure 1 provides an overview of the CSMCaD and its scientific goals. The upper portion illustrates a
flowchart of the four Specific Aims in Component 1 of the Center. These aims focus on the goal of understanding
the behavior of TIC, whose function is governed by the spatial and temporal ordering of multiple interacting
components at the molecular, cellular, and tissue levels. The experimental data will be used in Component 2,
depicted in the lower portion of the figure, to develop mathematical and computational models of TIC signaling
and behavior, including the use of mathematical equations and relationships as well as computer simulations to
represent and model biological phenomena, such as proliferation, apoptosis, cell migration, and treatment
response. These approaches serve two purposes. First, they provide a basic framework for the interrogation and
integration of data, often providing insight into the type and quality of data needed for addressing a hypothesis or
experimental design. Second, these models or simulations should allow one to predict the biological response of
TIC to an experimental therapeutic agent under investigation and to predict how TIC-related processes will
behave under different circumstances. The predictions generated in Component 2 can in turn be tested explicitly
in experiments conducted within Component 1. We will discuss the synergy of the Specific Aims between
Component 1 and Component 2 at the end of this Section.
Component 1 is guided by the hypothesis that TIC represent a unique sub-population of cells within a
tumor possessing properties of self-renewal and the ability to give rise to the characteristic cell types
present within a given tumor. Because of their unique abilities, we hypothesize further that TIC are
localized and function within a spatially and molecularly-regulated microenvironment (mE) (a.k.a.
niche). To identify, localize, and functionally interrogate TIC in vivo in sufficient detail to allow mathematical
modeling of their behaviors and responses to genetic and pharmacological manipulation in Component 2, the
Specific Aims of Component 1 are:
Aim 1.1: To identify tumor-initiating cells (cancer stem cells) using newly developed lentiviral
fluorescent signaling reporters and to characterize their spatial distribution and behaviors during tumor
growth using in vivo imaging.
Based on our current knowledge of TIC regulation by signaling networks, including Wnt, Notch, and Hedgehog,
we propose to use a novel set of lentiviral fluorescent signaling reporter vectors to identify, localize, and purify
TIC from both mouse and human mammary tumors based on activities of these and other pathways in the TIC
cells themselves. In addition to static histological preparations, individual stem cells can be tracked in live
animals using a combination of high-resolution confocal microscopy and two-photon video imaging methods.
Thus, the location and movement of TIC can be monitored over time at different phases of tumor development.
These analyses should be informative about interactions between TIC and their local environment, including
proximity to blood vessels, ECM, and interactions with stromal cell types, such as macrophages, neutrophils, and
fibroblasts. These data will be used to develop and validate the bio-mathematical model of TIC mE
(microenvironment) model that will be discussed in Specific Aim 2.1 of Component 2.
Aim 1.2: To identify candidate genes and pathways that may regulate TIC behaviors (e.g. self-renewal,
differentiation, and metastasis)
By using the new fluorescent signaling reporter vectors used or developed in Aim 1, as well as known cell surface
and enzymatic markers (e.g., CD44, CD24, and ALDH1), we will purify (or highly enrich) TIC populations away
from other non-tumorigenic cell types using Fluorescence Activated Cell Sorting (FACS). Microarray (Affymetrix)
and proteomic (antibody arrays, high-throughput immunofluorescence imaging) analyses will then be used to
obtain gene expression data for each different cell population. Data will be analyzed using advanced
bioinformatics methods (Component 2) to discover molecular pathways active in TIC and niche cell types. These
data describing the relationship between signal pathways and cellular identities will be used to refine the TIC mE
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model and predictive modes of Aim 2.1 and Aim 2.2 in Component 2, respectively. The genes identified or
predicted will then be tested functionally in Aim 3 of Component 1.
Aim 1.3: To conduct a “Directed Iterative Functional Genomic Screen” (DIFGS) to characterize genes
functionally that either increase or decrease tumor-initiating capacity.
Using a TIC gene expression signature defined previously using the CD44 and CD24 cell surface markers on
human clinical samples, we recently completed an initial functional genomics screen of 1,290 lentiviral shRNA
constructs targeting ~500 genes. This screen identified 101 genes regulating mammosphere formation (a
surrogate in vitro assay for TIC and normal stem/progenitor cell function). A similar study is underway using a
gene expression signature derived from TIC in mouse p53-null tumor models. We propose to extend these
screens in a directed, iterative manner by advanced bioinformatic approaches (Component 2) to define a new
candidate target list using the 101 genes as input to identify known or suspected interacting proteins, immediate
upstream regulators, and downstream targets. Additional unknowns from microarray data will also be tested
whenever possible (up to about 500 genes can be screened at one time). These new candidates will be tested
functionally using mammosphere-formation assays to identify only those genes regulating MSFE and the
process repeated for five iterations per species (~2500 genes each species), or until all bioinformatics-defined
interactions are exhausted. Human and mouse gene lists can then be mined for overlapping and unique gene
sets and tested in vivo in Specific Aim 1.4 described next. These data will be analyzed through advanced
bioinformatics methods described in Specific Aim 2.3 of Component 2, and the results can be used for the
validation of the refined model TIC mE in Aim 2.2.
Aim 1.4: To define the cellular responses of TIC to genetic and pharmacological manipulation of genes
regulating TIC survival or function in vivo.
Once key molecules are identified as functionally important in Aim 1.3 of Component 1, and the integrated
molecular and cellular model is built in Component 2, the response to genetic and pharmacological manipulation
of molecules in the model will be predicted, tested, and used to refine the model. Based on the premise that TIC
must be targeted specifically for development of effective treatment or prevention of breast cancer, discovery of
drugs that kill TIC specifically, or block their function will be critically important. Our ongoing work investigating
inhibitors of normal stem cell self-renewal (including inhibitors of Notch, Hedgehog, and the PI3K/Akt axis)
suggests that these agents function at the level of the TIC since they reduce the frequency of self-renewing cells,
but typically do not alter tumor volume significantly unless combined with cytotoxic systemic therapies. We
expect that a subset of the lentiviral shRNA constructs affecting TIC behavior in MS assays will have similar
activity against TIC function in vivo.
We will use a novel collection of mouse mammary tumors and low passage transplantable human xenografts
to study the effects of genetic (constitutive or doxycyclin-inducible lentiviral shRNA expression vectors) and
candidate pharmacological TIC inhibitors (currently in use or, suggested from analyses of Component 2) on TIC
behavior and frequency in vivo. Moreover, the combinatorial effects of shRNA knockdown or experimental
therapeutics with conventional chemotherapies will be examined with the goal of finding more effective cancer
treatments for individual breast cancer subtypes. These data will again be used for the development of the
drug-integrated model and for the validation for Aim 2.4 in Component 2.
Component 2 is guided by the hypothesis that TIC behavior during tumor development can be
simulated using a robust, multiparameter mathematical/computational model of TIC behavior during
breast cancer development. Further, that these models can be built to reflect not only the molecular,
cellular, and tissue-level dynamics, but also to allow prediction of the response of TIC to experimental
therapeutics. Thus, the central goal of Component 2 is to build a multi-scale model platform of TIC mE for
investigating TIC self-renewal, proliferation, localization, and other functions within a spatially and
molecularly-regulated microenvironment. Based on the experimental data obtained from Component 1 and
published knowledge of TIC, we will model the TIC tissue microenvironment (TIC mE) from the molecular and
cellular level up to the tissue level. The TIC mE model can further predict and guide the pathway analysis, the
candidate gene selection, genetic and pharmacological manipulation in Component 1. Accordingly, the
Specific Aims of Component 2 are:
Aim 2.1: To model the TIC tissue mE mathematically based on 2D and 3D microscopy and image
analysis
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The microenvironment, including cellular and non-cellular components, is well-known to play an important role in
supporting and influencing the behavior of TIC. Bio-imaging informatics models will be developed to quantify the
TIC tissue microenvironment images obtained from Component 1 and then TIC mE spatial distribution can be
modeled. Based on the quantified data as well as from the literature and online databases, we can apply ordinary
differential equations (ODEs) and more sophisticated differential equations to describe the relationship among
TIC and molecules, enzymes, nutrients and other cell types in microenvironment (e.g. fibroblasts, vasculature,
immune cells) mathematically in an effort to model tumor development in silico. This will be a model at the
cellular and tissue levels onto which the key molecular level mechanisms discovered in Aim 1.3 of Component 1
can be mapped in Aim 2.3. Therefore, further experiments will be carried out in Specific Aim 1.2 of Component 1
based on feedback from the results obtained in this aim.
Aim 2.2: To predict the TIC pathways or key genes related to specific cancer subtypes so to refine the
TIC microenvironment model
Bioinformatic analysis of DNA microarray and proteomic data generated in Specific Aim 1.2 of Component 1,
coupled with the genetic and pharmacologic manipulations of TIC function in Aims 1.3 and 1.4, will enable us to
identify key candidate components in the pathways that are related to cellular behavior and survival.
Subsequently, we will map these signaling pathway factors to specific tumor cell types and further to specific
cellular properties by modeling them as functions of the factors. For example, psy  f ( x1 ,..., xn ), pasy  g ( x1,..., xn ) ,
where x1 ,..., xn are genes/factors, and f and g are the functions that model the relationship between symmetric
or asymmetric self-renewal rates and the genes in TIC pathways. The TIC mE model will, in turn, be refined
based on the newly inferred pathway and network information. With the network of genes integrated into the
biomathematical model, predictions can be made by changing the parameter values for the network components,
so that a subset of key factors will be found. These predictions will guide the iterative functional genomics
experiments in Aim 1.3 of Component 1 to focus on the most likely gene candidates.
Aim 2.3: To develop bioimaging informatics models for mapping gene functional networks within and
among TIC and niche cells from the directed iterative shRNA screen and further refine the TIC mE model
We will develop bioinformatics models for discovering gene functional networks by integrating gene function
annotation results from the shRNA genome subset screening in Specific Aim 1.3 and publicly available
multi-modality genomic data. We will first develop an integrated image analysis system for shRNA screens and
score each gene based on the phenotypic information, then we will develop an image-based systems biology
approach to study the gene functional networks. Biological processes are often an orchestra of groups of genes,
and the gene functional network studies are important to understand and study gene functions. Combining with
the prior knowledge, the gene functional annotation results from the shRNA screen will have the potential to
identify known or suspected interacting proteins, immediate upstream regulators, and downstream targets. New
experimental data that are unanticipated by the model can be used to further improve our mathematical TIC mE
model.
Aim 2.4: To model the response of TIC and their microenvironment to genetic and pharmacological
manipulations of TIC function in vivo
Based on our ability to assay the relationship between exposure to signaling inhibitors and gene expression in
relatively pure cell populations, as well as the mathematical model linking molecular level data to the cellular and
tissue levels, we can adjust the model to predict the response of TIC to new drug candidates. Technologies will
be designed to elucidate, interrogate, and model the role of physical forces on varying cellular functions,
including cellular ligand-receptor interaction, cell proliferation, differentiation, cell cycle evaluation, apoptosis and
evolution of tumor phenotypes, or motility in order to facilitate an increased understanding of the role that
physical forces play in cancer pathology and metastasis. Under different conditions, e.g., metastasis or
non-metastasis stage, increased or decreased motility, changes in intracellular mechanics and ability of cells to
interact with the environment will all be included for modeling the distribution of tumor-initiating cells. The
collaboration of the Aim 2.4 and Aim 1.4 will be in an iterative manner to better refine the mathematical model in
order to derive more robust drug candidates for inhibiting or managing TIC.
Coherence and Synergy of Specific Aims between Component 1 and Component 2
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In this section, we provide a summary of the aforementioned Specific Aims and elucidate the coherence and
synergy between Component 1 and Component 2.
As proposed, Aim 1.1 of Component 1 will identify, localize, and purify TIC using newly developed
experimental biotechnologies and will also analyze the interactions between TIC and their microenvironment.
Armed with such information, in Aim 2.1 of Component 2, we propose to construct a biomathematical model
describing the cellular behavior of TIC and their interactions with other cellular and non-cellular components
surrounding them. Further experiments will be carried out in Specific Aim 1.2 of Component 1 based on the
feedback of the results obtained in Aim 2.1.
Next, in Aim 1.2 of Component 1, we will identify candidate genes and pathways that may regulate TIC
behavior by using genomic and proteomic analysis. Correspondingly, in Aim 2.2, we will use advanced
bioinformatics algorithms to identify key components identified in Aim 1.2, which form a pathway or network that
may regulate cellular behavior. Therefore, we can investigate models to describe the interactions in this network,
and then map these genes and proteins to specific cellular properties. In this way, we can refine our
mathematical TIC mE model derived in earlier Aim 2.1 by incorporating the function of gene networks. Since the
initial pathway network can sometimes be very complex and large, we will divide the network into several
sub-networks according to their functions for better navigation and manipulation. With the refined TIC mE model
in Aim 2.2 of Component 2, we can study the effects of all components in these pathway sub-networks by
changing their values in the mE model, through which we can predict the outcomes of up-regulation or
down-regulation of certain genes. Thus, we can find the key components in each sub-network, which can be
seen as hypotheses for biological mechanisms underlying cellular behavior. The genes in these sub-networks
will be also the candidate genes used for directed iterative shRNA screen in Aim 1.3 of Component 1.
To validate the hypotheses, Aim 1.3 will evaluate the functions of these found genes in Aim 2.2 on TIC
properties first by using mammosphere-formation cellular assays, a surrogate assay for TIC and progenitor cell
function. Corresponding bioimaging informatics techniques for analyzing these data are proposed in Aim 2.3. In
this way, new experimental data will be generated and analyzed to validate the predictions by the refined model
in Aim 2.2. This can also further refine our TIC mE model, which will be employed to guide the definition of
cellular responses of TIC to genetic and pharmacologic manipulation as well as drug response prediction.
After these Specific Aims are completed, an integrated and robust biomathematical model will be established,
including interactions from the sub-cellular to the cellular and tissue levels. Similarly, in Aim 2.4, we will first use
data from previous findings of the TIC mE model to incorporate the effects of drugs/shRNA into our model and
then predict the potential outcomes by modifying treatment-related parameters. In Aim 1.4, we will investigate
further the effect of the drugs (e.g. inhibitors of TIC) experimentally, with the purpose of generating data for
validation and improvement of our TIC mE model.
N1.2. CSMCaD Center Organization
The proposed Center for Systematic Modeling of Cancer Development (CSMCaD) is composed of a
multi-disciplinary team of investigators from several institutions across Texas Medical Center, including: The
Methodist Hospital-Weill Cornell Medical College, Baylor College of Medicine, and the University of Texas Health
Science Center (UTHSC) at Houston.
The Methodist Hospital, Baylor College of Medicine, and UTHSC at Houston are located within walking
distance of each other at Texas Medical Center. This geographic proximity provides great convenience for the
synergy and interaction among the Methodist-Cornell, Baylor, and UTHSC teams. Many of the team members
have been collaborating in a number of research projects on breast and other type of cancers, including those
requiring new techniques in computational biology, bioimaging, pathway inference, tumor invasion
microenvironment modeling, and computational modeling of drug treatment response.
The CSMCaD will be leaded by a group of established researchers with track records in managing larger
scale nationally allied projects, including the PI, Dr. Stephen Wong, and the other core PIs at the partnering sites,
Dr. Michael Lewis, Dr. Jeffrey Rosen, and Dr. Suzanne Fuqua at Baylor College of Medicine, Dr. Xiaobo Zhou at
Methodist, and Dr. Vittorio Cristini at UTHSC
The PI, Dr. Stephen Wong, John S. Dunn Distinguished Endowed Chair of Biomedical Engineering and
Professor of Bioengineering and Computer science in Radiology at Weill Cornell Medical College, is an
established scientist and seasoned project manager. He has extensive experience in leading national
biomedical research networks. Before he moved to the Methodist Hospital in May 2007, Dr. Wong was the Co-PI
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and management core PI of NAMIC (National Alliance of Medical Image Computing), a NIH National Center of
Biomedical Computing for the analysis and visualization of medical images. Dr. Wong was also the informatics
Co-Chair
for
fBIRN
(functional
Biomedical
Informatics
Research
Network) and a member of
fBIRN steering committee.
fBIRN is the integral part of
BIRN, another NIH roadmap
funded initiative that fosters
distributed collaborations in
biomedical
science
by
utilizing
information
technology innovations. Dr.
Wong brings to the CSMCaD
with more than two decades
of experience in building and
modeling
large
scale
systems and managing
scientific
and
product
development for leading
institutions in academia and
industry, including HP, AT&T
Figure 2. The organization structure of the Center for Systematic Modeling of Cancer
Bell Labs, Japanese Fifth
Development (CSMCaD)
Generation
Computer
Systems project, Philips Medical Systems, Charles Schwab, UCSF, and Harvard. He was a key member of the
pioneering UCSF PACS (picture archiving and communication system) program, headed scientific industrial
labs at Philips Research and product development departments of Philips Medical Systems, managed the
technology division of Charles Schwab, and created several research labs and centers during his tenure at
Harvard, including HCNR Center for Bioinformatics at Harvard Medical School, as well as the Functional and
Molecular Imaging Center, Optical Imaging Laboratory, and Conjugate and Medicinal Chemistry Laboratory at
Brigham and Women’s Hospital. He directed interdisciplinary teams of over 400 scientists, researchers, and
engineers globally while in industry. Dr. Wong received his executive education from MIT Sloan School, Stanford
University Graduate School of Business and Columbia University Graduate School of Business.
The core leadership team of the CSMCaD is composed by a group of established scientist and researchers
from multiple disciplines including computational biology, imaging bioinformatics, molecular biology, cancer
biology, imaging chemistry, bioengineering, biophysics and instrumentation, and clinical oncology, as well as
staff members in administrative and other supporting cores.
N1.3. Management
The PI, Dr. Stephen Wong, will direct and manage the daily operation and coordination across sub-teams of
the CSMCaD projects. The site Core PIs at Baylor College of Medicine will take the responsibility for Component
1: Experimental systems biology and the Core PIs at Methodist-Cornell and UT Health Science Center will be
responsible for Component 2: Computational biology and modeling predictive medicine. The PI will also work
with the core PI at Baylor, Dr. Lewis, on the Component 3 of the education and training, and, meanwhile, manage
the pilot projects together with the co-PI and project manager of the Administrative Core, Dr. Fei Cao of
Methodist-Cornell. The PI and Core PIs will have regular meetings and ad-hoc conversations on project
progression. The PI will assume the ultimate responsibility for ensuring smooth execution of this project, with the
Internal Advisory Committee (IAC) to assist in conflict resolution. The members of the internal advisory
committee will include Dr. Wong and the core PIs of the three partnering sites, Dr. Michael Lewis, Dr. Jeffrey
Rosen, Dr. Xiaobo Zhou, and Dr. Vittorio Cristini.
The center will also form an External Advisory Panel (EAP) to include three to five experts in the areas of
cancer cell biology and computational biology. This panel is primarily designed to provide feedback and
suggestions and will visit and interact with the center at least once a year. The EAP will also act as a counsel for
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the investigators in the management of resources, resolving potential administrative issues arise, and ensuring
the smooth integration of the Methodist-Baylor-UTHSC partnership. The selection of the EAP is to be made by
the PIs with consent from the NCI program officer. The applicants will identify the desirable profiles of
prospective EAP members.
We also invite a strong consultant team composed of well-known experts in the cancer biology, stem cell
biology, systems biology, bioinformatics, cancer microenvironment modeling, and in-vivo stem cell labeling,
including Professor Norbert Perrimon at Harvard Medical School, Professor Dihua Yu at UT MD Anderson
Cancer center, Professors Margaret Goodell and Daniel Medina at Baylor College of Medicine, Professor
Michael Zhang at Cold Spring Harbor Laboratory, Professor Muhammad Zaman at UT Austin, and Professor
Charles Lin at Massachusetts General Hospital (MGH), Harvard Medical School. They will contribute their
experience to guide the proposed project.
To ensure effective management, a relatively small leadership team (PI and Core PIs) will be formed to
coordinate primary projects, task-specific-projects and supporting core activities. This team will also bring many
facets of knowledge to bear upon the decision-making process, enabling faster, more effective decisions to be
made about shaping the direction of the scientific research of the CSMCaD. This will be especially important in
view of the demands of working with research groups across multiple disciplines and multiple institutions. The
small yet representative nature of the team will minimize the cost of overhead and ensure swifter
communications. Additional input to the decision-making process will come from the core leaders.
N1.4. Interdisciplinary Research Team
The proposed CSMCaD is composed of a multi-disciplinary team of investigators across three major
institutions at Texas Medical Center, i.e., The Methodist Hospital Research Institute, Baylor College of Medicine,
and the University of Texas Health Science Center. The expertise ranges from basic science such as cell biology
and cancer genetics to applied technology such as computational systems biology, in silico cancer cell-matrix,
cell microenvironment modeling, and software development; as well as clinical disciplines, such as clinical
pathology and oncology.
The PI, Dr. Wong and the core PIs, Drs. Lewis, Rosen, Zhou, Cristini, each brings complementary skills and
capabilities to the proposed Center for cancer system biology at Texas Medical Center. Dr. Wong is a
world-renowned leader in biomedical informatics and image computing while Drs. Lewis and Rosen have
expertise in the fields of breast development and function, as well as in breast cancer research – particularly in
the area of normal and malignant stem cell biology. Dr. Zhou has extensive research experience in
bioinformatics and image bioinformatics. Dr. Cristini has rich experience in tumor invasion modeling and drug
responsive simulation. Working together, the team will be responsible for the overall direction of the CSMCaD,
and the planning, management, coordination, and integration of all contract activities. They will also be
responsible for the scientific and technical leadership of CSMCaD, its implementation, interfacing with ICBP
staffs and subcontractors, and ensuring that deliverables and milestones are achieved according to established
timetables. Furthermore, the comprehensive expertise and experience of the investigator team is evidence that
this team is well-qualified to carry out the proposed project.
Stephen TC Wong, PhD (PI), please see description of the qualification and experience of the PI in Section
N1.2.
Fei Cao, PhD, (Project Manager), Director of Clinical Research Informatics Lab, Bioinformatics &
Biomedical Engineering Program, TMHRI and Assistant Professor of Bioinformatics in Radiology, WCMC, will
serve as a project manager to coordinate the management of the proposed center by working closely with Dr.
Wong and the co-PIs. Dr. Cao has over fifteen years of biomedical informatics and scientific project management
experience.
Michael Lewis, Ph.D. (Core PI), Assistant Professor of Molecular and Cellular Biology in the Lester and Sue
Smith Breast Center at Baylor College of Medicine. Dr. Lewis is trained in normal mammary gland development
and breast cancer. His main research focus is in the role of hedgehog signaling in the regulation of mammary
stem cells and functional differentiation at lactation. His more recent collaborative work with Drs. Jenny Chang
and Jeff Rosen has been in the area of identification of tumor initiating cells and characterization of their intrinsic
chemo-resistance phenotypes in clinical samples after treatment with conventional therapeutics. In addition, Dr.
Lewis has expertise in in vivo analysis of experimental therapeutics and the effect of experimental therapeutics
on the tumor-initiating population. Dr. Lewis has long-standing collaborations with Drs. Chang, Rosen,
Hilsenbeck, and Edwards, and has collaborated with Dr. Wong and his groups over the past few years.
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Dr. Lewis will also serve as the Core co-PI for the educational component in collaboration with Dr. Suzanne
Fuqua. Dr. Lewis has extensive teaching experience at the undergraduate and graduate level, as well as training
experience at the graduate and postdoctoral levels.
Jeffrey Rosen, Ph.D. (Core PI), C.C. Bell Professor of Molecular and Cellular Biology, BCM Dr. Rosen is an
internationally recognized leader in the area of hormonal regulation of mammary gland development, stem cells
and the molecular biology of mammary gland gene expression. Dr. Rosen’s laboratory, in collaboration with Dr.
Peggy Goodell at Baylor College of Medicine, was the first to identify functional stem/progenitor cells markers in
the normal mammary gland. His laboratory has extended these studies to the characterization of tumor initiating
cells (TICs) in a p53 null mouse model of breast cancer, and using microarray and functional assays have
identified intrinsic differences in cell cycle checkpoints and DNA repair pathways in TICs. In collaboration, with
Dr. Charles Perou’s laboratory Drs. Rosen, Jenny Chang, and Michael Lewis at BCM have shown that
tumor-initiating cells (TICs) often termed “cancer stem” cells (TICs) defined in human breast cancers share many
common genomic patterns with a new molecular subtype called the “claudin-low” subtype, which was identified
by comparative mouse-human oncogenomics. A tumorigenic signature derived from the TICs was present as a
small population in all breast cancer subtypes revealed by the analysis of residual tumor cells post-therapy. This
collaborative study is currently in press in the Proceedings of the National Academy of Sciences.
Xiaobo Zhou, Ph.D. (Core PI), Associate Professor of Bioinformatics in Radiology, Weill Cornell Medical
College and Chief of Bioinformatics and Bio-image Computing Laboratory, Bioinformatics and Biomedical
Engineering Program, TMHRI, will serve as a core PI of the Component 2 of CSMCaD. He is an expert of
applying advanced mathematics, pattern recognition, computer vision, signal processing, and data mining
techniques in analyzing and modeling biological data and images, particularly those generated from high
throughput biotechnology such as genomics, proteomics, tissue arrays, and high content screening. He and Dr.
Wong pioneered the field of image bioinformatics and image-based system biology. They lead the development
of a family of new image bioinformatics packages for cell biology and neurobiology studies. They have recently
co-authored one of the first books in Computational Systems Bioinformatics.
Vittorio Cristini, Ph.D. (Core PI), Associate Professor, the School of Health Information Sciences at
UTHSC at Houston, will serve as a core PI of the Component 2. Dr. Cristini is also affiliated with the
Bioengineering Departments at UT Austin and UT MD Anderson Cancer Center. His group seeks to integrate
experimental and computational methods in an effort to investigate tumor biology. Currently, he focuses on
examining the role of tumor micro-environmental spatial and temporal heterogeneity in promoting invasive and
eventually metastatic cancer phenotypes. His group develops and applies multi-scale, predictive, computational
cancer models based on well-established principles of physics, mathematics, and cancer biology that utilize
state-of-the-art numerical techniques. This integrative framework allows us to form and test hypotheses that
drive experimental investigation, which in turn provides data to refine our biomathematical models.
Suzanne A.W. Fuqua, Ph.D (Core PI). Professor of Medicine, The Lester and Sue Smith Breast Center. Dr.
Fuqua is a co-PI for the Breast Center training grant, the course director for the Translational and Clinical Breast
Cancer course, and is an internationally recognized leader in the areas of estrogen receptor function in breast
cancer, hormone therapy resistance, and metastasis. Dr. Fuqua has extensive training experience at the
graduate and postdoctoral levels. She will be an invaluable resource for multiple aspects of the project,
particularly the educational component, which she and Dr. Lewis will oversee jointly.
Mary Dickinson, Ph.D., Associate Professor, BCM, will serve as a co-investigator to guide in-vivo imaging.
Her laboratory uses a multi-disciplinary approach, including microscopy, molecular biology, and fluid mechanics,
to study the role of fluid-derived mechanical forces in vascular remodeling and heart morphogenesis in early
vertebrate embryos; her lab has developed methods for time-lapse, confocal imaging of rapid blood flow and
heart mechanics using vital fluorescent protein reporters. Her lab is a part of the Molecular Physiology and
Biophysics Department at BCM
Jenny Chang, M.D., Medical Director of the Lester and Sue Smith Breast Center and Professor of Medicine
of the Baylor College of Medicine and Chief of Breast Medical Oncology, Ben Taub General Hospital will serve as
a co-investigator to guide clinical aspect of the CSMCaD project. Dr. Chang has extensive clinical and laboratory
experience in the area of therapy resistance, gene expression analysis for the prediction of treatment response,
and evaluation of experimental therapeutics, both pre-clinically and clinically.
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Thomas Westbrook, Ph.D., Assistant Professor of Biochemistry and Molecular Biology at BCM. Dr.
Westbrook was recently recruited to Baylor from Dr. Steven Elledge’s laboratory at Harvard University and brings
with him extensive expertise in the use of lentivirally-delivered shRNA. Dr. Westbrook is the Director of the Cell
Based Assay Screening Service (C-BASS) shRNA core facility and has conducted genome-wide shRNA
screens similar to the Directed Iterative Functional Genomics Screen proposed. Dr. Westbrook has also
spearheaded the development of novel inducible lentiviral shRNA vectors for use specifically in mammary
epithelium, vectors that will be used extensively in Component 1, Aim 1.4.
Susan G. Hilsenbeck, Ph.D, Professor of Medicine and Director of Biostatistics and Bioinformatics in the
Lester and Sue Smith Breast Center at BCM, will serve as a co-investigator to guide the statistic analysis. Dr.
Hilsenbeck is an internationally recognized biostatistician with extensive experience in clinical trial design,
microarray analysis, and preclinical study design. Dr. Hilsenbeck will work extensively with Dr. Shaw, and both
will serve as intellectual and communication bridges between the laboratory based component 1 and the
mathematical and computational modeling based Component 2 of this proposal.
Chad Shaw Ph.D., Assistant Professor of Genetics at BCM, will serve as a co-investigator to be responsible
for the initial genomics and proteomics data analysis generated in Component 1. His research interests are
systems biology and the analysis of large scale genomic data. His group analyzes primary microarray data sets
from all array platforms including expression arrays, genome content arrays (aCGH), microRNA arrays, and
chromatin arrays with an expertise in data pre-processing and normalization.
Dean P. Edwards Ph.D., Professor of Molecular and Cellular Biology, BCM, will sever as a co-investigator to
be responsible for the statistic analysis Dr. Edwards is an internationally recognized expert in hormonal
regulation of breast cancer and gene expression. He has extensive experience in development of monoclonal
antibodies, protein biochemistry, and more recently high-throughput proteomics analyses. Dr. Edwards is the
director of the Proteomics Shared Resource of the Dan L. Duncan Cancer Center at Baylor College of Medicine.
He will work closely with Drs. Huang and Engler for the proteomic analyses proposed herein.
Shixia Huang, Ph.D., Assistant Professor of Molecular and Cellular Biology, is an expert in proteomic
analyses, particularly using protein and antibody microarrays. In addition, she has extensive knowledge of issues
related to breast cancer and mammary gland development. She will work closely with Dr. Edwards and Dr.
Engler for the proteomic analyses proposed.
Ching Tung, Ph.D., Professor of Radiology, WCMC and Director of Diagnostic and Imaging Probes Lab,
TMHRI, will serve as a co-investigator to guide the in-vivo TIC cell labeling in mouse model and assist Dr. Wong
(PI) to manage the pilot projects. Dr. Tung’s research focuses on creating molecular probes to detect biomarkers
in various types of diseases. Dr. Tung has pioneered the field of optical molecular probes for in vivo imaging.
Over the past few years, he has applied the development of novel multi-functional molecules to molecular
sensing, in vivo molecular imaging, therapy, and drug delivery.
Jeff (Chung-Che) Chang, M.D., Ph.D., Professor of Pathology & Laboratory Medicine, WCMC and Director
of Hemopathology, TMHRI, will serve as a co-investigator, participate the TIC mE modeling, pathway inference,
and data analysis. Dr. Chang is a hematopathologist with research interest in myeloma, diffuse large B-cell
lymphoma, and myedysplastic syndromes. Dr. Chang focuses on clinical and translational focusing on
identifying markers and signaling pathways that are important for diagnosis, prognosis and treatment of
hematological malignancies. Dr. Chang has a rich experience in DNA/cDNA/tissue microarray analysis, flow
cytometry, meloma stem cells, and molecular diagnostic techniques. He will guide the data analysis and
algorithmic development.
John Baxter, M.D., Professor of Medicine, WCMC, Director of Genomic Core and Co-Director of the
Diabetes Center, TMHRI; and Chief of Endocrinology, the Department of Medicine, TMH, will serve as an
investigator. He was Chief of the Division of Endocrinology and Director of Metabolic Research Unit at UCSF.
He also co-founded several startup companies in biotechnology. Dr. Baxter will provide guide for the software
development and data analysis.
David Engler, Ph.D. Director of the Proteomics Core at TMHRI will serve as an investigator. He will be
responsible for guiding protein-level quantification and verification work necessary to validate or confirm the
genomics-level data that the CSMCD will be analyzing. The Proteomics Core will also provide assistance in
protein-level pathway analysis stemming from systems-level biological data directly correlated to, or inferred
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from many of the genomic, epigenomic, or transcriptomic changes observed in the cancers studied by the
CSMCaD.
Paul Macklin, Ph.D., Assistant Professor of Health Informatics at the School of Health Information Sciences
at the University of Texas Health Science Center – Houston, will serve as a co-investigator to work on cancer mE
modeling. He works at the intersection between biology, medicine, mathematics, and computer science to
develop and validate sophisticated computer models of cancer. He works under Vittorio Cristini on several
research projects in the field of computational and predictive oncology.
Xiaofeng Xia, Ph.D, Instructor, Medical Systems Biology Lab, The Methodist Hospital Research Institute,
will serve as a co-investigator in the Component 2 to guide the TIC mE modeling. He completed his postdoc in
University of Wisconsin – Madison working in stem cell research and subsequent spending three years as a
research scientist in WiCell working under Dr. James Thomson. He has worked in a number of areas including
bioinformatics, neurotransmitter releasing and neuronal differentiation of stem cells.
N1.4. Consultants
In addition, we have assembled a team of leading experts in bioinformatics, cancer biology, computational
biology, computational genomics, stem cell biology and systems biology as consultants to this project. These
consultants will serve as “thought leaders” for their respective expertise and for the CSMCaD as a whole to guide
the development of biologically-related pathway analysis and data analysis. They will also be called upon to beta
test new software and provide feedback on the current CSMCaD performance. The consultants include

Dr. Margaret Goodell, Professor and Director of STaR Center, Baylor College of Medicine, a leader
scientist in the basic biology of hematopoietic stem cells, the behavior study of stem cells in vivo and in
vitro using mouse stem cells as a model;

Dr. Daniel Medina, Professor of Molecular and Celllular Biology, Baylor College of Medicine Dr.
Medina is an internationally recognized leader in mammary gland development and preneoplastic breast
disease;

Dihua Yu, MD, PhD, Nylene Eckles Distinguished Professor and Vice Chair of Molecular and
Cellular Oncology and Director of Cancer Biology Program at M.D. Anderson Cancer Center, a
leader in understanding breast cancer initiation, metastasis, therapeutic resistance, apoptosis, cell cycle
control, signal transduction, cancer stem cell-like properties, microRNA deregulation, cancer
deregulation of cellular metabolism, and cancer molecular imaging of breast cancer;

Michael Zhang, PhD, Professor of Computational Biology and Bioinformatics at Cold Spring
Harbor Laboratory, a well-known expert in building a comprehensive network of the genes involved in
the regulation of growth and homeostasis which can provide a "system level" understanding of gene and
pathways;

Norbert Perrimon, PhD, Professor of Genetics at Harvard Medical School, a leader in
whole-genome RNA interference (RNAi) and other chemical genetics screens using our high-throughput
screening and pathway analysis;

Dr. Charles Lin, Associate Professor of Wellman Center for Photomedicine & Center for Systems
Biology, Massachusetts General Hospital & Harvard Medical School, a leader in cancer stem
labeling in in-vivo animal model, In-vivo monitoring of cell trafficking in circulation, imaging of vasculature
and microenvironment in tissue, interaction of cells with microenvironment;

Dr. Muhammad H. Zaman, Assistant Professor of Department of Biomedical Engineering, The
University of Texas at Austin, a leading scientist in modeling cancer cell microenvironment to
understand how cancer cells interact with the extra cellular matrices in native environments by employing
computational and experimental tools of biophysics, cell biology, mechanics and chemistry to study
cancer related problems.
N1.5. Timeline for the overall program
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The outline of defining how novel analytical tools will be developed and applied to ICBP data will be generated
and submitted to the NCI within the first three months of the project. We request five years to complete the
proposed project. The timeline of this project is listed in the next Table 1.
Table 1: The estimated timeline of the CSMCaD project.
Tasks
Year 1
Year 2
Year 3
Year 4
Year 5
TIC identification, labeling & distribution study, TIC mE image
analysis and TIC mE modeling
TIC distribution study, TIC mE modeling, genomics,
proteomics & mechanism study in regulating TIC, TIC
Pathway inference, and TIC mE remodeling
TIC mE modeling, mechanism study in regulating TIC via
pathway analysis, shRNA interference screening, target
discovery for regulating TIC
shRNA interference screening, bioimaging informatics for
candidate target discovery, and TIC mE refining
Integration study based on in-vitro/in-vivo information; TIC mE
modeling and prediction for drug treatment response
Section N.2. Administrative Core
The specific aim of the administrative core is to provide a flexible yet effective administrative structure to
support the infrastructural and scientific aims, in view of the many faceted interactions that must necessarily
occur among the CSMCaD teams. To accomplish this aim, we will develop and execute a management plan
based on a balanced management strategy that supports an environment of shared decision-making and mutual
responsibility among the core PIs, while providing the oversight and leadership necessary to produce quality
biomedical imaging work. We will manage the overall CSMCaD project using sound basics, including phased
delivery, quick and concrete feedback, clear articulation of the project needs, project tracking and oversight,
effective governance, and inter-group coordination.
The overall organization and administrative structure of this CSMCaD project is shown in Figure 2. The PI,
Dr. Stephen Wong and the project manager (PM) Dr. Fei Cao, along with Ms Sample and Ms. Roberts will direct
and manage the daily operation and coordination across subteams of the project. The four specialized cores
including the administration core, experimental system biology core, computational biology core, and education
& training core, will provide the infrastructure to execute and support the proposed research projects. The PI,
PM, and Core PIs will have regular meetings to interact with each other. The PI will assume the ultimate
responsibility for ensuring smooth execution of this project, with the internal advisory committee to assist in
conflict resolution. The members of the advisory committee and the management are described in Section N1.3.
N.2.1 Management Plan
The management plan encompasses two balanced goals: Effective Management and Oversight of Quality
Research Work.
Effective management: The goal for effective management includes the creation of a relatively small
leadership team (PI and Core PIs), which will be responsible for coordinating primary project,
task-specific-projects and supporting core activities. This team will also bring many facets of knowledge to bear
upon the decision-making process, enabling faster, more effective decisions to be made about shaping the
direction of the scientific research. This will be especially important in view of the demands of working with
research groups across multiple collaborative institutions. The small yet representative nature of the team will
minimize the cost of overhead and ensure swifter communications. Additional input to the decision-making
process will come from the core directors.
Oversight of quality research work: Each of the cores will set its own goals and competencies that will
dovetail with the overall CSMCaD objectives. To a great extent, various research projects will run themselves. It
will be the role of the leadership team of CSMCaD to shape the overall scientific direction and to bring projects
back in line when they go off course, as sometimes occurs in a multidisciplinary, multi-institutional research
setting.
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The management strategy that emerges for the proposed CSMCaD is one of decentralized decision-making
rather than centralized control. The mission of the leadership team is to make the entire multi-disciplinary project
a success. To that end, the tactics derived from the balanced goals of the management strategy include (1)
providing a relatively horizontal organizational structure as opposed to the vertical hierarchy that typifies most
corporate organizations; (2) implementing project management, quality controls, and service controls; (3)
enabling autonomous units; (4) emphasizing flexibility and responsiveness; (5) collecting performance metrics to
ensure that metrics are reviewed by those whose work is being measured; (6) embracing changes when
necessary; and (7) intervening occasionally in situations that cannot be resolved from a distance. These are the
characteristics of organizations that succeed over time. Accordingly, the specific aim of the leadership team of
CSMCaD will be accomplished by: (1) establishing an effective management structure; (2) deploying project
tracking and oversight mechanisms; (3) administrating overall center budgets and fiscal matters; (4)
documenting progress reports and accomplishments; (5) organizing and scheduling meetings, including annual
all-hands meetings; regular committee meetings; regular core meetings and teleconferences; and ad hoc
meetings among investigators of different projects and cores; (6) providing and reinforcing guidelines and
policies regarding human subject protection, inclusion of women, minorities, and children in research, care and
use of vertebrate animals in research, software licensing, intellectual property, and publication of peer-reviewed
scientific papers; (7) reinforcing the NIH data-sharing policies; and (8) coordinating and interfacing with the ICBP
steering committee and other ICBP centers.
N.2.2. Organizational Roles of Cores
Administration core: A highly effective administrative resource is critically important in the successful
establishment of this CSMCaD. This core will be the administrative center of the Methodist-Baylor-UTHSC team.
The administrative core resource will consist of the PI, the Core PIs, a Research Project Coordinator and an
Administrative Assistant. The CSMCaD advisory committee will communicate directly with this resource in terms
of monitoring progress, providing evaluation and counseling the PI in issues arising during the administration of
the Center.
The administrative core resource will coordinate the administration of the CSMCaD, organize the steering
and advisory committee meetings, and track milestones and project progress. Retreats, symposia, seminars,
and meetings will be coordinated and organized through this resource. Monitoring and reconciliation of the
various budgets, and facilitation of the purchase of supplies will be also be provided by this resource.
The CSMCaD project team consists of seventeen investigators spread over three institutions. Efficient
communication and a high level of interaction will be achieved through the administrative resource which will
include the maintenance of an interactive Wiki project web site and annual all-hands meetings. Furthermore,
email listings where daily postings on day-to-day activities and information will be provided.
The administration core will also have support from TMHRI administration resources. Under the direction of
Edward Jones, M.B.A., Vice-President in charge of administration, TMHRI has a fully developed and staffed
research administration and support infrastructure. Among its innovations is web-based management of the
document flow for the Institutional Review Board and other research administration functions. TMHRI is fully
compliant with all NIH Grants Policies and OHRP policies regarding human research subjects.
The administration core will ensure synergies and resource sharing of Component 1 & Component 2.
The weekly meeting between these two components will take place as usual. The core will also make sure
management of component 3 -the Education & Training is going well, see details in Section N5.
N.2.3. Management of the Research Workshop
Annual Interdisciplinary Symposium: The Methodist, Baylor, and UT teams will host an annual 2-day
symposium at Texas Medical Center featuring keynote speakers that will include local, national, and international
leaders in relevant systems and cancer biology research, as well as leaders from the other ICBP centers and
members of external advisory panel. We will feature live demonstrations of the site, portal, and analysis tools,
and hands-on tutorial workshop training sessions. Annotation jamborees also will be scheduled during this event
to establish community consensus and standardization of terms or nomenclature. Scholarships will be available
to assist young investigators (students and post-doctoral fellows) with travel, lodging, and registration expenses
related to the event.
Houston presents as an ideal location for such an event for several reasons: 1) it is the hub for Continental
Airlines, one of the nation’s largest, which means that investigators can easily travel here via nonstop flights from
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most cities, including many major foreign cities; 2) Houston’s central location in the country allows attendees to
avoid long, transcontinental flights; and 3) hotels in Houston are more reasonably priced than those in most other
major American cities.
N.2.4 Management of Pilot projects
The TMHRI will actively solicit opportunities to collaborate with the scientific community on biological studies
of cancer stem cell microenvironment that involves the application of high-throughput technologies, modeling,
and bioinformatics techniques. The Pilot Projects or the Driving Biological Projects (DBP) funding mechanism
will fund projects that have the potential to significantly contribute to the field of cancer stem cell
microenvironment and enhance the capabilities and usefulness of the CSMCaD. TMHRI will seek to fund
transformative projects whose outcomes will have implications for a broad array of cancer systems biology. For
example, these projects could include the in-vivo cancer stem cell and other cell labeling, drug combination
prediction, development of new modeling of tumor metastatic tissue, development of new analytical tools for next
generation sequences, the deep characterization of drug resistance, and novel software to integrate in vivo
imaging data with other data modalities, such as optical microscopy data. The CSMCaD plans fund four projects
in the year 1 and year 2, 3 proposals in year 3 and 4, and 2 proposals in year 5 and totally 16 one-year projects
over the 5-year period. The first four projects will start in August of 2010. We will post an announcement open to
all experimental laboratories in the United States once the CSMCaD is funded. Interested investigators will be
invited to submit their proposals. The review panel will consist of the PI, Core PIs and the members of external
advisory panel. We anticipate that the TMHRI Bioinformatics and Biomedical Engineering Program will provide
necessary bioinformatics support to the awardees.
Solicitation and Review of Proposals: The strategy that we will use for solicitation and review of proposals
is analogous to one that TMHRI and Baylor currently use successfully to manage several internal seed funding
proposal programs. White paper proposals will be solicited from the scientific community through
announcements posted on the CSMCaD website, and other outreach activities including ads in scientific journals
and flyers and posters distributed and posted at scientific meetings. The Pilot Projects or DBP Program will
especially be advertised to all cancer and related investigators in USA. The DBP white page guidelines will also
be included in the solicitation. The proposals will be submitted electronically via the streamlined Methodist Online
Research Technology Initiative (MORTI) system of TMHRI that forwards the applications to the Research Project
Coordinator, then to reviewers, grants and staffs for budget review. TMHRI investigators currently use this facile
electronic system extensively. Award notification and grant management are also managed by the MORTI
system.
We will first review the proposals for responsiveness to the solicitation. Proposals deemed responsive to the
solicitation will then be sent out to external expert reviewers selected by the management committee. We will use
an NIH-style peer-review panel process to rank order the proposals. Reviewers will assign priority scores to the
proposals and return them to the Project Manager within one month. The four applications with the best priority
scores will then be forwarded on to the ICBP Project Officers for their review and approval. The Research Project
Coordinator, with input from the management committee, will address any questions or concerns the officers
may have regarding the projects and their management in her Final DBP Project Plan to be submitted to the
officers within 2 weeks of officer approval of the projects. Her plan will include monitoring procedures for the
projects and will establish metrics and milestones. The monitoring procedures will likely emulate a U-series
reporting structure and include semi-annual reports to be submitted to the Project Coordinator one month prior to
the due date of the BRC semi-annual progress report. Upon receipt of written approval of the Final DBP Project
Plan from the PI and ICBP officers, funds will be dispersed to grantees.
N2.5. Existing Supporting Cores (Environments and Resources)
Resources of The Methodist Hospital Research Institute. A complete account of available resources for
this proposal is provided in the resource pages of this proposal. Briefly, TMHRI Cores participating in the ICBP
Project include Bioinformatics and Biomedical Engineering Programmatic Core (Stephen Wong, Director and PI
on this project and Xiaobo Zhou, Lab Chief and a core PI), Cellular and Tissue Microscopy Core (Stephen Wong,
Director and PI on this project), Animal Imaging Core (Stephen Wong, co-Director and PI), Genomics Core (John
Baxter and Paul Webb, Director and co-Director as well as co-investigators on this project), and Proteomics Core
(David Engler, Director and co-investigator on this project). These specialized cores at TMHRI provide the
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infrastructure to execute and support the proposed research projects. Resources of the UT Health Science
Center are provided in the resource pages.
Resources of the Baylor College of Medicine A detailed list is also provided in the resource pages.
Briefly, the institutional core facilities available to support this center are the Gene Expression Core
(microarray/qPCR), Proteomics Core (Dean Edwards, Director and co-investigator on this project), C-BASS
Core (Thomas Westbrook, Director and co-investigator on this project), Cytometry and Cell Sorting Core, Vector
and Virus Production Core, Integrated Microscopy Core, the Genome Sequencing Center, and the Genetically
Engineered Mouse Core. In addition to these institutional core facilities, the Lester and Sue Smith Breast Center,
in which Dr. Michael Lewis is a faculty member, has an Animal Handling and Imaging Core (Michael Lewis,
Director and core PI on this project), Microarray Core, qPCR Core, Pathology Core, and a
Bioinformatics/Biostatistics division (Susan Hilsenbeck, Director and co-investigator on this project). Thus,
several of the institutional and center-based core directors are active participants in this project. In addition,
Baylor College of Medicine has an interinstitutional agreement with the MD Anderson Cancer Center which
allows full use of MD Anderson Core facilities by faculty at Baylor at subsidized prices.
The site or core PIs will assume the oversight responsibilities for individual core resources in their institutions.
The CSMCaD PI will assume the ultimate responsibility for ensuring smooth and streamlined operating of the
core resources. The supporting core directors will work closely with CSMCaD members to ensure access to the
core resources. The PI and the core directors will also have regular meetings to interact with each other.
N.2.6. Interaction with other ICBP centers
Our ICBP working group is headed by the PI, Dr. Wong. It facilitates interactions and collaborations in order
to foster learning and improvements among the Centers which could then be also applied to the other data
generated by other groups. The CSMCaD will work under the guidance of the ICBP Steering Committee on the
development, harmonization, and standardization of methods for data collection and analysis across the different
platforms to be developed by centers in the ICBP network. The CSMCaD will work closely with other ICBP
centers to identify and test methods suitable for performance validation of multi-scale data acquisition and
multimodal imaging, leading to multi-center, multi-platform clinical studies involving all centers in the ICBP
network. The PI and the CSMCaD research group leaders will participate in the ICBP working groups for the
purpose of communicating information across the network centers relevant to joint activities and creation and
maintenance of network-wide resources.
N2.7. Compatibility with caBIG
The software tools and data models developed in the CSMCD will be made compatible to the NCI caBIG
infrastructure, according to the caBIG Compatibility Guidelines. The interoperability between the CSMCaD tools
and caBIG software would be planned to be at least at the silver level such that the barrier to use CSMCaD
software by a third party will be significantly reduced.
N2.8. Plan for Sharing Research Data, Resources, and Intellectual Property
Plan for Sharing Research Data, Resources, and Intellectual Property: All primary data, datasets,
algorithms, and protocols generated will be conducted at the PI’s labs (Bioinformatics and Biomedical
Engineering Program, including Laboratory for Medical Systems Biology and Laboratory for Bioinformatics and
Bio-image Computing) at TMHRI and Core PIs’ labs at Baylor and UTHSC. All data pertaining to this project will
be deposited into the CSMCAD database. Data generated as part of this research project will be made freely
available after publication. Our labs have an excellent track record of sharing reagents with the community.
As in previous studies, we will present and disseminate our research results, software package, and related
publications and documentation on the public CSMCAD website hosted by the PI’s lab. We will post step-by-step
instruction including screen shots and test images for users to download and run by themselves. On the
CSMCAD website, we set up a “Contact Us” for users to send us questions and comments. Users may choose to
register with us online by just using their email addresses (registration is not required to download the software).
We will notify registered users by email about new release and upgrade, in addition to post the messages online.
All research data and analysis tools generated under this award will be made publicly available to the
research community. This includes all data generated through the driving biological projects and all software and
analysis tools developed for pathway analysis, data integration from different sources, and so on. All data will be
released once they are verified. Any software algorithms and programs developed by the center and our
collaborators will be made publicly available in a manner consistent with the goals of NIH for software
dissemination, and unique biological information (DNA sequences, etc.) will be submitted to caBIG for wide
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dissemination to the research community. Research tools (including analysis tools, algorithms, software
interfaces, source codes and other
software technologies) developed or
enhanced with contract support will be
made available in caBIG and the CSMCaD
web portal after they have been
appropriately tested both internally and by
our cadre of expert consultants. The
CSMCaD is a collaborative program, and
TMHRI and its Baylor/UTHSC partners will
work within the guidelines established by
the NIH and with other funded sites to
prepare a joint dissemination plan upon
notice of award.
Intellectual
Property:
TMHRI-Baylor-UTHSC will be responsible
for acquisition of all proprietary and
intellectual property rights needed to
perform the projects proposed herein.
TMHRI will work with our subcontractors to
expedite the acquisition processes and
stay on track with agreed upon milestones.
TMHRI has dealt with multi-institutional IP
issues as part of our 670-page Clinical and
Translational Science Award application
submitted in 2008.
Figure 3. The 92 gene taxotere sensitivity predictor (Left) did not
predict AC response (Right) The expression levels are shown in red
(expression levels above the median for the gene) and blue
(expression levels below the median for the gene).
Section N.3. Previous Accomplishments for New Applicants
N3.1. Accomplishments in Stem Cell Biology and Breast Cancer Research
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A
B
CD44+/CD24 30
Mammosphere Formation
Efficiency
130
n=31
120
n=31
110
No. of MS/10,000 cells
25
CD44+/CD24-
Gene
expression
patterns
correlating with response to
different
chemotherapeutic
regimens. As part of our previously
funded SPORE grant project
(Chang/Lewis), we generated gene
expression signatures for the
prediction of response to Docetaxel
[1],
and
anthracyclin-based
chemotherapy in breast cancer
patients [2], as well as genes
regulated by hedgehog signaling
agonists and antagonists in breast
cancer cell lines (M.T. Lewis, in
preparation).
Docetaxel is one of the most active
agents in breast cancer, but
resistance or incomplete response
is frequent. To determine whether
there existed a gene expression
pattern prior to treatment that could
predict for sensitivity to Docetaxel,
core biopsies from 24 patients were
obtained before treatment with
neoadjuvant
docetaxel,
and
20
15
10
100
90
80
70
60
50
40
30
5
20
10
0
0
Initial
Week 3
Observed
Week 12
Predictive
Initial
Week 3
Observed
Week 12
Predictive
Figure 4. Effect of chemotherapy on the mean percentage of cells that
express high levels of CD44 and low levels of CD24 (CD44+/CD24 >) as well
as mammosphere formation efficiency among HER2-negative patients
before, during, and after treatment. Circles represent observed values.
Predicted values (dashed lines) and their 95% confidence intervals (CIs;
thin error bars) were estimated by linear mixed-effects models. Error bars
on circles represent 95% CIs (two SEMs) of experiments at baseline and
each time point of follow-up. A) Percentage of tumorigenic cells increased at
week 3 (P < .001, model-based contrast) and remained high at surgery (week
12) (P < .001, model-based contrast). Statistical tests were two-sided. B)
Effect of chemotherapy on mean mammosphere (MS)-forming efficiency
before, during, and after treatment. All patients, P < .001, model-based
contrast.
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response was assessed after chemotherapy. After 3 months of neoadjuvant chemotherapy, surgical specimens
(n = 13) were obtained, and laser capture microdissection (LCM; n = 8) was performed to enrich for tumor cells.
From each core, surgical and LCM specimen, total RNA was extracted for cDNA array analysis using the
Affymetrix HgU95-Av2 GeneChip microarrays.
From the initial core biopsies, differential patterns of expression of 92 genes correlated with docetaxel response
(P = .001) (Figure 3, Left). However, the molecular patterns of the residual cancers after 3 months of docetaxel
treatment were strikingly similar, independent of initial sensitivity or resistance. This relative genetic homogeneity
after treatment was observed in both LCM and non-LCM surgical specimens. The residual tumor after treatment
in tumors that were initially sensitive indicates selection of a residual and resistant subpopulation of cells. The
gene expression pattern was populated by genes involved in cell cycle arrest at G2M (eg, mitotic cyclins and
cdc2) as well as markers later thought to serve as markers expressed in breast cancer “stem cells”. Of
importance, the taxotere sensitivity signature did not predict response to Doxorubicin/cyclophosphamide (AC)
response (Figure 3, Right) [2]. Thus, sensitivity to AC will be dependent on an entirely different set of genes that
are currently under analysis.
Intrinsic chemo-resistance of tumor-initiating sub-populations of breast cancer cells. Based on results of
data mining of the taxotere and AC response data suggesting enrichment of cells expressing putative stem cell
markers after conventional treatments, we recently demonstrated in clinical samples that tumorigenic breast
cancer cells are enriched in residual tumors after chemotherapy, but not after lapatinib treatment. In matched
human breast cancer biopsies (n= 31 pairs), the relative proportion of CD44+/CD24-/low cells (previously
characterized as enriched for tumor-initiating cells [3]) increased with chemotherapy from a baseline mean of
4.7% to 13.6% after 12 weeks of chemotherapy (p<0.0001), indicating enrichment of chemotherapy-resistant
potentially tumorigenic breast cancer cells (Figure 4A). Consistent with the increase in the relative proportion of
CD44+/CD24-/low cells, mean MSFE was significantly increased after chemotherapy in matched pre- and
post-chemotherapy samples (p<0.001) (Figure 4B), indicating an enrichment for cells capable of
anchorage-independent growth.
Unlike with chemotherapy, lapatinib treatment of HER2+ breast cancers did not increase the proportion of
CD44+/CD24-/low breast cancer cells, but led to a statistically non-significant decrease in matched biopsies from a
baseline mean of 10.6 to 7.4% (p=0.1) after 6 weeks of lapatinib (Figure 5A). Also unlike with chemotherapy,
MSFE did not increase with lapatinib treatment, but showed a non-significant decrease (Figure 5B). Consistent
with the effect of lapatinib on tumorigenic cells, use of lapatinib to augment conventional therapy increased the
pathological complete response rate
by 3- to 4-fold compared to the
A
B Mammosphere Formation
CD44+/CD24published rate with conventional
Efficiency
therapy alone. Thus, lapatinib
appears to target the TIC population
with equal efficiency as the bulk of
the tumor. If true, lapatinib
represents the first characterized
“stem cell” targeted therapy against
HER2+ breast cancer.
Gene expression analysis of
enriched TIC populations in
mouse mammary tumor models.
Using the collection of stably
transplantable
mouse
tumors
derived from p53 null mammary
epithelium
carried
in
the
epithelium-free mammary fat pad of
host mice, the Rosen laboratory
(co-investigator) has demonstrated
that TIC in these tumors are
characterized by expression of both
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Figure 5. Effect of lapatinib on mean percent cells that express high amounts
of CD44 and low amounts of CD24 (CD44+/CD24>) and mammosphere
(MS)-forming efficiency (MSFE) before, during, and after treatment. Circles
represent observed values. Predicted values (dashed lines) and their 95%
confidence intervals (CIs; thin error bars) were estimated by linear
mixed-effects models. Error bars on circles represent 95% confidence
intervals (two SEMs) of experiments at baseline and each time point of
follow-up. A) Percentage of CD44+/CD24> cells in samples from biopsy
cores. B) MSFE of samples from biopsy cores.
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the CD29 and CD24 cell surface antigens in FACS analysis. Cells that are double positive for both CD29 + and
CD24+ possess tumor-initiating function, while the other three cell populations (CD29+;CD24-, CD29-;CD24+, or
CD29-;CD24-) are greatly diminished in this ability. As few as 20 double positive cells are capable of forming new
tumors upon transplantation, whereas several thousand cells in the other populations are required.
Using FACS coupled with gene expression microarray analysis, the Rosen laboratory has generated a gene
expression signature of p53 null tumor-initiating cells from three individual tumors (Figure 6, from [4]) and has
identified 710 probesets differentially expressed in common among the three models used. Similar to the
lentivirus-based shRNA knockdown strategy described below for the human, we are in the process of conducting
a functional genomic analysis of these genes using lentiviral shRNA expression vectors to determine which of
these genes are required for TIC function in p53 null tumors. Results of this functional screen can then be
correlated with those of the human for commonalities and differences among the models.
Wnt signaling and radiation resistance of tumor-initiating cells. Based on the observation that
stem/progenitor cells, which
remain after breast cancer
therapy and may give rise to
recurrent disease, we (Rosen)
hypothesized that progenitor
cells are resistant to radiation,
a component of conventional
breast
cancer
therapy.
Further, that resistance is
mediated at least in part by
Wnt signaling, which has been
Figure 6. Differentially expressed transcripts in tumor-initiating cells of p53
implicated in stem cell
transplantable mammary tumors. A, Venn diagram of transcripts differentially
survival.
To
test
this
expressed
in
Lin_CD29HCD24H
compared
with
Lin_CD29HCD24L,
hypothesis, we investigated
Lin_CD29LCD24H,
and
Lin_CD29LCD24L
subpopulations
of
p53-null
transplantable
radioresistance by treating
mammary
gland
tumors
(P
<
0.01
for
each
comparison).
B,
the
heat
map of 710
primary
BALB/c
mouse
differentially expressed transcripts in the tumorigenic cancer cell Lin_CD29HCD24H
mammary epithelial cells with
subpopulation. Each row represents a transcript; each column represents various
clinically relevant doses of
subpopulations from three tumors. The red color indicates high level expression,
radiation.
We observed
whereas blue indicates a low level of expression. The top five IPA-picked molecular
enrichment
in
normal
and cellular functions in which those down-regulated and up-regulated genes
progenitor cells (stem cell
involved are indicated on the left (number of molecules).
antigen 1-positive and side
population progenitors) capable of clonal growth. Consistent with a role for canonical Wnt signaling, radiation
selectively enriched for progenitors in mammary epithelial cells isolated from transgenic mice with activated
Wnt/β-catenin signaling but not for background-matched controls, and irradiated stem cell antigen 1-positive
cells had a selective increase in active β-catenin and survivin expression compared with stem cell antigen
1-negative cells.
Functional genomics screening technology. A unique advantage to our project is the availability of Dr.
Westbrook. Dr. Westbrook did his postdoctoral training at Harvard Medical School in the laboratory of Steve
Elledge, Ph.D., where he developed (in collaboration with the Hannon lab) genome-wide retroviral RNA
interference (RNAi) libraries (known as the Hannon-Elledge shRNA libraries). Using barcoding technology,
these libraries enable rapid and functional interrogation of the genome in ways that cannot be achieved by
current siRNA-based “well-by-well” approaches. Using these technologies, Dr. Westbrook devised a new
strategy to identify human tumor suppressors systematically on a genome-wide scale [5]. This highly cited work
was successful in identifying known and novel tumor suppressors, thus, providing a new method for discovering
this class of cancer genes. One result of this work was the discovery of REST as a tumor suppressor which has
opened up a new field of studying how neuronal regulators control breast cancer growth and survival, work that
Dr. Westbrook has recently expanded [6] as an independent investigator at Baylor.
Real-time in vivo imaging capabilities. Another unique aspect of this project is the time-lapse and real-time in
vivo imaging capabilities developed in the laboratory of Dr. Mary Dickinson. Dr. Dickinson has developed
methods for imaging vascular development during embryonic stages of development using a combination of
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two-photon and advanced confocal imaging technologies. She has also developed a number of specialized
image analysis methods for studying the vascularization process in 3D, methods that can be modified (if
necessary) to model neovascularization of tumors as they develop in the mouse mammary fat pad. These
methods should also allow us to localize fluorescently
tagged TIC relative to the vasculature and track their
behavior over time.
N3.2. Accomplishments in Computational Biology
We (Wong labs) have been working in
computational biology and imaging bioinformatics for
decades and are well established in the following areas
related to this proposal: PACASS database system,
cellular and molecular high-content screening and data
analysis, computational biology for biomarker
discovery and signaling pathways analysis.
N.3.2.1. Image Bioinformatics Platforms Developed
D-CELLIQ – Dynamic cellular imaging quantitator
Bioimage Data Generation and Analysis: We
successfully
imaged
HeLa,
N-tert1
cells
Figure 7. D-CELLIQ 1.0 graphical user interface.
(telomerase-immortalized keratinocytes), and Hct116
cells on our time-lapse microscopy system. Cells were treated with drugs of different concentrations. Images are
captured as TIFF files first using software
developed by Compix, which also controls
the microscope, stage, and camera.
These TIFF files are then transmitted to
our in-house built D-CELLIQ 1.0 system for
image processing and analysis. HeLa
H2B-GFP cells were thawed six days
before plating for each experiment and
cultured in DMEM with 10% FBS. The
details of the protocols can be found from
[7, 8].
D-CELLIQ 1.0 consists of eight core
modules: image acquisition, nuclei
detection, nuclei segmentation, nuclei
tracking, feature extraction, cell phase
identification, statistical analysis of cell
cycle behaviors, and output all information
into a database. D-CELLIQ 1.0 has been
released to the public since early 2008,
see http://dcelliq.cbi-platform.net.
Here we describe our major
achievements. Since cells often cluster
Figure 8. The representative lineage tree structures of cell division
together, the detection of cell nuclei prior to
with time-lapse microcopy analyzed with D-CELLIQ: asymmetric (left
segmentation is non-trivial. To circumvent
panel) vs. symmetric division (right panel). The numbers in green
this problem, D-CELLIQ 1.0 includes a new
nodes (representing the time point when cell division is captured) or
detection algorithm that comprises three
images are the number of nuclei in the corresponding imaging frames
steps:
binarization,
local
maxima
acquired at different time intervals after starting imaging as indicated
generation, and searching for local
by the black numbers. The upper row of images are captured 15
minutes before cell division is identified (middle row of images) to
maxima[9, 10]. Highly accurate cell
illustrate the metaphase of dividing cells. The bottom row of images
tracking is a challenging issue [11-13].
shows the tracked cells at the end of imaging (50 hours). Cell #21 (left
Over or under segmentation, fast
panel) appears to go through an asymmetric division while cell #28
movement of cells, and the problem of
(right panel) goes through a symmetric division.
overcrowded cells would cause tracking
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error. Parallel tracking is being studied and developed [14]. We developed two sophisticated tracking algorithms,
and both of them achieve good performance [15, 16]. Figure 7 shows a snapshot of the D-CELLIQ 1.0 interface
while Figure 8 shows the automated tracking sequence with symmetric and asymmetric division.
Feature extraction and cell-cycle phase identification: After obtaining the segmented nuclei, feature
vectors are generated in D-CELLIQ 1.0 to represent the cells. Each feature vector contains 211 features. These
features are composed of 11 general image features, such as shape, size, and intensity (including max intensity,
min intensity, deviation of gray level, average intensity), length of long axis, length of short axis, long axis/short
axis, area, and perimeter [13]; 14 Haralick co-occurrence textural features [17]; 47 Zernike moment features [18];
85 features generated by Gabor transformation [11, 19]; and 54 shape features. To remove the irrelevant
features and improve the performance of the learning system, a prediction risk-based feature selection method is
employed in our previous study to choose the sub-optimal feature sets[20, 21]. This method adopts an
embedded feature selection criterion of prediction risk that evaluates features by calculating the change if the
corresponding feature is replaced by its average value. It has several advantages. First, the embedded feature
selection model depends on learning machines. Fifty-eight features are kept for cell phase identification,
comprised of 37 Gabor features, 1 geometric feature, 14 moment features, 2 texture features and 4 shape
features. The geometric feature used is "perimeter." Gabor features can describe the nuclear both in the time
and frequency domain, thus many Gabor features are kept.
Modeling of cell-cycle mitotic phases: Different methods of feature extraction, feature section, and
classifiers for phase classification are investigated in D-CELLIQ 1.0, including new methods such as
Context-based hidden Markov model (CBHMM) [7] and on-line support vector machine (OSVM) [8]. An Online
Support Vector Classifier (OSVC) algorithm was developed in D-CELLIQ 1.0 to remove support vectors from the
old model and assign the new training examples with different weights according to their importance. In addition,
phase identification in D-CELLIQ 1.0 takes into consideration of the temporal-spatial patterns of nuclei dynamics.
Each time region contains prophase, metaphase, and anaphase as a temporal-spatial pattern that is reflected by
the phase sequence (temporal) and morphologic nuclei appearance (spatial). A dynamic programming algorithm
was developed to identify the phase patterns that best satisfy the prophase, metaphase, and anaphase ordering.
More results can be found from[8, 13, 22, 23].
G-CELLIQ – whole genome RNAi cellular imaging quantitator
G-CELLIQ enables processing large volumes of digital images generated from high throughput RNAi screen
applications and reduces the time required in processing the images from months in manual analysis to hours or
minutes on a computer. Current ability of our G-GellIQ includes: (1) Integrated cell image processing pipeline; (2)
capability of handling images from three different channels; (3) automatic two-step cell segmentation with
nuclear segmentation supplying seed region for cell body segmentation; (4) morphological feature extraction
describing each cell using 211 features
from 5 categories; (5) feature selection
method using SVM (Support Vector
Machine)-RFE, Genetic Algorithms and
unsupervised methods like k-Nearest
Neighborhood; (6) online phenotype
discovery using mixture model and gap
statistics; (7) cell classification using
SVM and graphical visualization; (8)
scoring cell groups based on the output
of different classifiers; and (9) gene
function analysis using cluster analysis.
One user interface of this system is
shown in Figure 9.
Computational architecture of
G-CELLIQ:
The
computational
functions of G-CELLIQ are organized
into: 1) image processing and cell
morphology
quantification;
2)
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Figure 9. A simple graphical user interface of G-CELLIQ.
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phenotype modeling and cell classification; and 3) Image scoring and gene function annotation. The output of
modules 1) and 3) comprises the database of quantified cells morphology and image/gene function score profiles,
respectively.
Integrated image processing pipeline: An efficient, compatible and robust image processing pipeline is
constructed, and when running on an Intel Core (TM) 2, 2G Hz, 2G RAM PC, it can handle large volumes of raw
images from a 384-well plate (5,384 images, ~100 cells each) within a day and generate quantified morphology
profile for each cell (~half million cells in total). The workflow includes a two-step cell body segmentation scheme
utilizing adaptive thresholding[24, 25], an over-segmentation correction method based on feedback systems[24],
and an automatic image quality control system. A group of 211 morphological features [25] are defined and
extracted to quantify each cell segment. This workflow is applicable to various HCS datasets upon tuning on a
small group of parameters.
Phenotype identification, cell classification, and gene function scoring: An original online phenotype
discovery method [26] is used to identify and validate novel phenotypes and finally form a stable phenotype
panel for each experiment. A subset of features are selected using SVM-RFE to differentiate each phenotype
from others, and a series of SVM classifiers are used to classify each cell segments into different phenotypes
[25]. The probability output of SVM classifiers are recorded as each cell segment’s similarity to different
phenotypes. Combining the scoring schemes in [25, 27], the scores for each cell are in turn summarized as
morphology scores for each image, well (each having 16 images) and RNAi treatment conditions (TC, each
represented by 2-8 wells), and finally consolidated through different biological replicates TC to form a functional
score for a single gene (each represented by 2-9 biological replicate TCs).
User interface and functional validation: The available functions are integrated into a GUI shown in Figure
9 to form the present G-CELLIQ workflow. This workflow is applied to an RNAi HCS on part of the KP
(kinase-phosphatase) set aiming at building signaling network
regulating cell shape change. Datasets from 16 384-well plates are
analyzed in about six months, and hierarchical clustering analysis
is carried out based on functional scores for each involved gene.
The clustering results, shown with some typical images from
different clusters as in Figure 10, restored some well-known
functional sub-groups and also indicate the role of previous
uncharacterized genes. Comparing with the data analysis scheme
in a similar screening [27], G-CELLIQ shows its efficiency and
promising future in the analysis of RNAi HCS.
We also developed another two software packages. One is
NeuronIQ – Neuron image quantitator. The software is freely
available from http://www.methodisthealth.coTYPE and can
accurately detect the central line of dendritic backbones and spines
of in vitro and in vivo 3D neuron image data from the noisy data by
curvilinear structure detector. Another one is N-CELLIQ – Neuron
and cellular image quantitator. The software aims to quantitate
and interpret automated fluorescence microscopy images
accurately and automatically, in particular, for the labeling and
measurement of neurites in 2D and 3D space. The 2-D version of
N-CELLIQ has been released for public on our website at
http://www.cbi-tmhs.org/software.html.
Figure 10. Hierarchical clustering results
based on gene function scores on KP set.
N.3.2.2. Accomplishments in Computational Genomics and Proteomics for biomarker discovery and
signaling pathways analysis
We have extensive experience in genomics data analysis [28, 29]. As an example, for one project studying
the variations of DNAs and RNAs in cancer patients, we developed several new models for SNP analysis
[30-32], eQTL mapping [33], and microRNA regulation [34]. In SNP analyses, we employed a new model which
explicitly considers the distance between two neighboring SNPs, genotyping error rate and heterozygous rate to
presents a novel LOH inference and segmentation algorithm based on the conditional random pattern (CRP)
model [30]. For a particular disease MDS (myelodysplastic syndromes), we employed a novel Constraint Moving
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Average (CMA) algorithm to detect the Copy Number Aberration (CNA) regions of sorted marrow SNP array
samples. Consequently, we have two independent applications based on the results of the CMA algorithm. The
first one is power analysis to determine the minimum sample size required in the experimental design. The other
is a General Variant Level (GVL) score for the discrimination of patients suffering from high and low grade MDS
[31, 32]. In the analyses of eQTL mapping, we constructed the MDS disease gene network by integrating the
expression quantitative trait loci (eQTL) mapping and the human interactome data [33]. In microRNA regulation
analyses, we developed a network inference model, called significance analysis of microRNA-mRNA targeting
(SAMiMT). In this model, the microRNA:gene binding probability is evaluated based on expression and
sequence data, followed by the estimation of gene collaboration probability on transcriptional abundance and the
microRNA binding compositions of genes [34].
At TMH, proteomics research focuses on the discovery of protein-level biomarkers including Stroke and
Major Adverse Cardiac Events (MACE) patients. At BCM, work is currently focused on kinase activation in
cancer cells and blood-borne biomarkers for cancer detection, and is operationally similar to work ongoing at
TMH. For stroke studies [35], we proposed a novel automatic peak detection method for the stroke MS data. In
this method, a mixture model is designed to model the spectrum of mass spectrometry proteomic data. A
Bayesian approach is used to estimate parameters of the mixture model, and a Markov chain Monte Carlo
method is employed to perform Bayesian inference. Another detection approach for MALDI mass spectrometry
study was also developed by employing time-frequency analysis approach [36]. For MACE studies, we
described one method for biomarker panel discovery [37] and another one for network biomarker discovery [10].
Conventional biomarker discovery focuses mostly on the identification of single markers and thus often has
limited success in disease diagnosis and prognosis. One new method is to identify an optimized protein
biomarker panel based on MS studies for predicting the risk of MACE in patients [37]. We presented a new
variant of GA that embeds the recursive local floating enhancement technique to discover a panel of protein
biomarkers with far better prognostic value for prediction of MACE than existing methods, including the one
approved recently by FDA (Food and Drug Administration). The other novel method is to identify the network
biomarkers based on protein-protein interactions to classify MACE patients from control patients [10]. We first
built up a cardiovascular-related network based on protein information coming from protein annotations in
Uniprot, protein-protein interaction (PPI), and signal transduction database. Aided by protein-protein interaction
network, that is, cardiovascular-related network, we proposed a new type of biomarkers, network biomarkers,
composed of a set of proteins and the interactions among them. The candidate network biomarkers can classify
the two groups of patients more accurately than current single ones without consideration of biological molecular
interactions.
N.3.2.3 Accomplishments in Cancer Cell Invasion Modeling and Modeling of Drug Responsiveness
Tumor growth and invasion: Cancer is the second most common cause of death in the United States,
exceeded only by heart disease, according to the American Cancer Society [38]. Mathematical modeling and
computer simulation are tools that can provide a robust framework to understand cancer progression better.
Cristini and colleagues develop and apply multi-scale, predictive, computational models of tumor growth and
invasion founded upon well-established principles of physics, mathematics, and cancer biology that utilize
state-of-the-art numerical techniques (see refs [39-45], references therein). The in silico parameter values that
govern the predictive cancer simulators are set according to in vitro and in vivo experimental evidence [41, 42,
46, 47]. This integrative framework allows us to form and test hypotheses that drive experimental investigation,
which in turn provides data to refine our mathematical models.
Cristini (co-PI on this project) and colleagues were among the first to advance modeling of complex tumor
morphologies beyond the limited capabilities of mathematical linear analysis and into the realm of nonlinear
computer simulation [40]. A biologically founded, multi-scale, mathematical model in [46, 47] is developed to
identify and quantify tumor biologic and molecular properties relating to clinical and morphological phenotype
and to demonstrate that tumor growth and invasion are predictable processes governed by biophysical laws, and
regulated by heterogeneity in phenotypic, genotypic, and micro-environmental parameters. In this model, the
behavior of cancer cells and their surroundings is linked to tumor growth, shape and treatment response. In the
work [39], they developed, analyzed and simulated numerically a thermodynamically consistent mixture model
for avascular tumor growth. The mixture model takes into account the effects of cell-to-cell adhesion,
chemotaxis, haptotaxis, and transport of important molecular species (e.g., oxygen and chemotherapeutic drug
compounds). In other recent work, Cristini and colleagues advanced the state-of-the-art in linking tumor growth,
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nutrient transport, hypoxia, release of angiogenesis-regulating factors (e.g., VEGF-A), and tumor-induced
angiogenesis, along with tumor-induced biomechanical changes in the microenvironment and neo-vasculature
that affected subsequent tumor-host-vasculature dynamics [43]. Cristini and colleagues also presented a
two-part series on multispecies nonlinear tumor growth in which they develop, analyze, and simulate a diffuse
interface continuum model of multispecies tumor growth and tumor-induced angiogenesis in two and three
dimensions[45, 48]. In the first part [48], they presented simulations of unstable avascular tumor growth in two
and three dimensions and demonstrate that their techniques now make large-scale three-dimensional
simulations of tumors with complex morphologies computationally feasible. In the second part [45], they
investigate multispecies tumor invasion, tumor-induced angiogenesis, and examine the effect of variable cell-cell
adhesion due to changes in cell phenotype and microenvironmental conditions (e.g., oxygen levels) and also
focus on the morphological instabilities that may underlie invasive cellular phenotypes.
Modeling for prediction of tumor drug response: The heterogeneity and three-dimensionality of the
tumor microenvironment presents a challenge to drug assessment, both during development and in the clinic,
consequently hindering development of effective therapies. However, a multiscale computer simulator founded
on the integration of experimental data and mathematical models can provide valuable insights into these
processes and establish a technology platform for analyzing the effectiveness of drug treatments, with the
potential to cost-effectively and efficiently screen drug candidates during the drug-development process. Cristini
and colleagues have groundbreaking work on these issues (see refs. [42, 46, 49]). In [49], Cristini and
co-workers describe their integrative approach to develop higher order bio-computational models , which has the
capacity to predict in vivo tumor growth and response to therapy. They implement an extensive
multi-compartment pharmacokinetics/pharmacodynamics model whose parameters are calibrated via published
experimental data to investigate the pharmacokinetics and effect of doxorubicin and cisplatin in vascularized
tumors [42]. This enables a comparison of the tissue and cell-level drug dynamics of the two drugs, and
facilitates the generation of hypotheses to explain their in vivo characteristics. Indeed, the methodology
presented herein could, with additional development, be applied to both established and nascent drugs to the
end of refining clinical trials and assisting in clinical therapeutic strategy to improve patient comfort and survival.
The multi-scale mathematical drug response model was recently implemented in [46] to successfully predict the
effects of doxorubicin on breast tumor growth in human MCF-7 cell lines. This model hypothesizes specific
functional relationships linking tumor growth and regression to the underlying phenotype, incorporates the
effects of local drug, oxygen, and nutrient concentrations within the three-dimensional tumor volume, and
includes the experimentally observed resistant phenotypes of individual cells to determine whether a prescribed
drug will reach the tumor in sufficient quantities to kill the malignant cells. This integrative method, tightly coupling
computational modeling with biological data, enhances the value of knowledge gained from current
pharmacokinetic measurements and augments efforts to predict drug response. Further, such an approach
could predict resistance based on specific tumor properties and thus improve treatment outcome.
N.4. Research Program (Integrated research effort of Components 1 and 2)
In this proposal, we seek to use newly developed experimental and imaging methodologies to identify,
localize, and purify tumor-initiating cells (TIC). This will then allow us to identify and image TIC in vivo, and to
model TIC behavior during tumor development with respect not only to spatial localization and movement, but
also with respect to specific changes in gene expression and cellular signaling. Combined functional genomics
and data mining strategies will allow us to characterize novel growth regulators. Further, our combined
experimental and systems biology approach will guide these biological experiments and allow us to evaluate
responses to experimental therapeutics that may inhibit or kill TIC specifically in a manner not possible before.
Aside from a wealth of basic biological insight, future extensions of this work may allow drug repositioning as well
as development of directed, mechanism-based and “stem cell”-centric drug screening and evaluation methods.
Although we introduce the two components in Sections N4.1 and N4.2 separately, the specific aims of the two
components are closely integrated as indicated in Figure 1.
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N4.1 Component 1 – Experimental System Biology
Title: Functional analysis of tumor-initiating cells in cancer development and treatment response.
Project Leaders (Core PIs): Michael Lewis, M.D., Ph.D, Jeffrey Rosen, Ph.D.
Project Summary:
A major obstacle to effective treatment of breast cancer is the realization that breast cancer is not a single
disease, but a collection of similar diseases, each is represented by the presence of a specific breast cancer
subtype. Systemic therapies such as chemo- or radiation therapy are effective initially in controlling and
reversing tumor growth. However, residual cancers will invariably re-grow despite this initial response. While
there have been several advances in the treatment of breast cancer in the last two decades, notably targeted
therapy for breast cancers expressing estrogen receptor (ER+) or the HER2 (ErbB2) oncogene, breast cancer
survivorship has improved only modestly. Unfortunately, for women with “triple negative” breast cancers (lacking
expression of ER, progesterone receptor (PR), and HER2) we currently have no targeted therapies.
Our recent clinical data, as well as experimental evidence in both mouse mammary tumors and human
breast cancer xenograft models, supports the existence of a subpopulation of cancer cells present in the original
tumor that are greatly enriched in residual cancers after conventional systemic therapies. These residual cancer
cells are characterized by their intrinsic resistance to chemotherapy and relative growth quiescence. However, a
discreet subset of these residual cells possesses enhanced self-renewal capacity, as well as the ability to form
tumors upon transplantation. These residual tumor-initiating cells (TIC) (a.k.a. cancer stem cells (CSC)) may
therefore be responsible for tumor growth, maintenance, resistance to treatment, and disease relapse. If this
hypothesis is correct, the failure of traditional systemic therapies, such as radiation and chemotherapy, to cure
breast cancer may be due to the fact that they incorrectly target the highly proliferative cells, while allowing
survival of treatment-refractory tumor-initiating “cancer stem” cells. These findings modify our conceptual
approach to oncogenesis and have dramatic implications for breast cancer prevention, treatment, and drug
development.
In this proposal, we seek to build upon, and significantly extend, ongoing laboratory and clinical studies using
newly developed experimental and imaging methodologies to identify, localize, purify and characterize TIC. This
will then allow us to identify and image TIC in vivo, and to model TIC behavior during tumor development
mathematically with respect not only to spatial localization and movement, but also with respect to proliferation,
apoptosis, and specific changes in gene expression and cellular signaling. Combined functional genomics and
data mining strategies will allow us to characterize novel growth regulators. Further, our combined experimental
and systems biology approach will allow us to evaluate responses to experimental therapeutics that may inhibit
or kill TIC specifically in a manner not possible before. Aside from a wealth of basic biological insight, future
extensions of this work may allow drug repositioning as well as development of directed, mechanism-based and
“stem cell”-centric drug screening and evaluation methods.
Key Personnel (listing percent of effort of each individual)
Michael T. Lewis, PhD. (Core PI) 20% effort Component 1, 5% Education/Training; Jeffrey M. Rosen, PhD.
(Core PI) 10%; Jenny C. Chang, M.D. (Co-investigator) 5%; Thomas Westbrook, Ph.D. (Co-investigator) 10%;
Susan Hilsenbeck, Ph.D. (Co-investigator) 5%; Chad Shaw, Ph.D. (Co-investigator) 20%; Dean P. Edwards,
Ph.D. (Co-investigator) 10%; Mary Dickinson, Ph.D. (Co-investigator) 5%; Shixia Huang, Ph.D.
(Co-investigator), 10%; Alejandro Contrerras, MD (Co-investigator) 10%;
Melissa Landis, Ph.D. (Research Assoc.) 25%; Lacey Dobrelecki. (Research Assoc.) 25%;
Bhuvanesh
Dave, Ph.D. (Postdoc) 80%; Jason Herskowitz, Ph.D. (Postdoc) 100%; Mei Zhang Ph.D. (Postdoc) 100%;
Tegy Vadakkan. Ph.D. (Postdoc) 20%; Shirley Small (Research Tech.) 25%; Wei Wei (Graduate student) 100%;
Kristen Meerbry (Graduate student) 50%;
N.4.1.A Specific Aims
Component 1 is guided by the basic hypothesis that TIC represent a unique sub-population of cells
within a tumor possessing properties of self-renewal and the ability to give rise to the characteristic cell
types present within a given tumor. Because of their unique abilities, we hypothesize further that TIC are
localized and function within a spatially and molecularly-regulated microenvironment (mE) (a.k.a.
niche). To identify, localize, and functionally interrogate TIC in vivo in sufficient detail to allow mathematical
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modeling of their behaviors and responses to genetic and pharmacological manipulation in Component 2, the
Specific Aims of Component 1 are:
Aim 1.1: To identify tumor-initiating cells (cancer stem cells) using newly developed lentiviral
fluorescent signaling reporters, and to characterize their spatial distribution and behaviors during
tumor growth using in vivo imaging.
Based on our current knowledge of TIC regulation by signaling networks, including Wnt, Notch, and Hedgehog,
we propose to use a novel set of lentiviral fluorescent signaling reporter vectors to identify, localize, and purify
TIC from both mouse and human mammary tumors based on activities of these and other pathways in the TIC
cells themselves. In addition to static histological preparations, individual stem cells can be tracked in live
animals using a combination of high-resolution confocal microscopy and two-photon video imaging methods.
Thus, the location and movement of TIC can be monitored over time at different phases of tumor development.
These analyses should be informative about interactions between TIC and their local environment, including
proximity to blood vessels, ECM, and interactions with stromal cell types, such as macrophages, neutrophils, and
fibroblasts. These data will be used to develop and validate the mathematical model of TIC mE
(microenvironment) model that will be discussed in Specific Aim 2.1 of Component 2.
Aim 1.2: To identify candidate genes and pathways that may regulate TIC behaviors, e.g. self-renewal,
differentiation, and metastasis
By using the new fluorescent signaling reporter vectors used or developed in Aim 1, as well as known cell surface
and enzymatic markers (e.g., CD44, CD24, and ALDH1), we will purify (or highly enrich) TIC populations away
from other non-tumorigenic cell types using Fluorescence Activated Cell Sorting (FACS). Microarray (Affymetrix)
and proteomic (antibody arrays, high-throughput immunofluorescence imaging) analyses will then be used to
obtain gene expression data for each different cell population. Data will be analyzed using advanced
bioinformatics methods (Component 2) to discover molecular pathways active in TIC and niche cell types. These
data describing the relationship between signal pathways and cellular identities will be used to refine the TIC mE
model and predictive modes of Aim 2.1 and Aim 2.2 in Component 2, respectively. The genes
identified/predicted will then be tested functionally in Aim 3 of Component 1.
Aim 1.3: To conduct a “Directed Iterative Functional Genomic Screen” (DIFGS) to characterize genes
functionally that either decrease tumor-initiating capacity or increase tumor-initiating capacity.
Using a TIC gene expression signature defined previously using the CD44 and CD24 cell surface markers on
human clinical samples, we recently completed an initial functional genomics screen of 1,290 lentiviral shRNA
constructs targeting ~500 genes. This screen identified 101 genes regulating mammosphere formation (a
surrogate in vitro assay for TIC and normal stem/progenitor cell function). A similar study is underway using a
gene expression signature derived from TIC in mouse p53-null tumor models. We propose to extend these
screens in a directed, iterative manner by making use of advanced bioinformatic approaches (Component 2) to
define a new candidate target list using the 101 genes as input to identify known/suspected interacting proteins,
immediate upstream regulators, and downstream targets. Additional unknowns from microarray data will also be
tested whenever possible (up to about 500 genes can be screened at one time). These new candidates will be
tested functionally using mammosphere-formation assays to identify only those genes regulating MSFE and the
process repeated for five iterations per species (~2500 genes each species), or until all bioinformatics-defined
interactions are exhausted. Human and mouse gene lists can then be mined for overlapping and unique gene
sets and tested in vivo in Specific Aim 1.4 described next. These data will be analyzed through bioinformatics
methods described in Specific Aim 2.3 of Component 2, and the results can be used for the validation of the
refined model TIC mE in Aim 2.2.
Aim 1.4: To define the cellular responses of TIC to genetic and pharmacological manipulation of genes
regulating TIC survival or function in vivo.
Once key molecules are identified as functionally important in Aim 3 of Component 1, and the integrated
molecular and cellular model is built in Component 2, the response to genetic/pharmacological manipulation of
molecules in the model will be predicted, tested, and used to refine the model. Based on the premise that TIC
must be targeted specifically for development of effective treatment or prevention of breast cancer, discovery of
drugs that kill TIC specifically, or block their function will be critically important. Our ongoing work investigating
inhibitors of normal stem cell self-renewal (including inhibitors of Notch, Hedgehog, and the PI3K/Akt axis)
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suggests that these agents function at the level of the TIC since they reduce the frequency of self-renewing cells,
but typically do not alter tumor volume significantly unless combined with cytotoxic systemic therapies. We
expect that a subset of the lentiviral shRNA constructs affecting TIC behavior in MS assays will have similar
activity against TIC function in vivo.
We will use a novel collection of mouse mammary tumors and low passage transplantable human xenografts
to study the effects of genetic (constitutive or doxycyclin-inducible lentiviral shRNA expression vectors) and
candidate pharmacological TIC inhibitors (currently in use or, suggested from analyses of Component 2) on TIC
behavior and frequency in vivo. Moreover, the combinatorial effects of shRNA knockdown or experimental
therapeutics with conventional chemotherapies will be examined with the goal of finding more effective cancer
treatments for individual breast cancer subtypes. These data will again be used for the development of the
drug-integrated model and for the validation for Aim 2.4 in Component 2.
N.4.1.B Background and Significance
Systemic therapies such as chemo- or radiation therapy are effective initially in controlling and reversing tumor
growth. However, residual cancers will invariably re-grow despite this initial response. While there have been
several advances in the treatment of breast cancer in the last two decades, notably targeted therapy for breast
cancers expressing estrogen receptor (ER+) or the HER2 (ErbB2) oncogene, breast cancer survivorship has
improved only modestly. In particular, for women with “triple negative” breast cancers there is currently no
targeted therapies.
Our recent clinical data [3], as well as experimental evidence in both mouse and human xenograft models [4, 50]
supports the existence of a subpopulation of cancer cells present in the original tumor that are greatly enriched in
residual cancers after conventional systemic therapies. These residual cancer cells are characterized by their
intrinsic resistance to chemotherapy and relative growth quiescence. However, a discreet subset of these
residual cells possesses enhanced self-renewal capacity, as well as the ability to form tumors upon
transplantation. These residual tumor-initiating cells (TIC) may therefore be responsible for tumor growth,
maintenance, resistance to treatment, and disease relapse. If this hypothesis is correct, the failure of traditional
systemic therapies, such as radiation and chemotherapy, to cure breast cancer may be due to the fact that they
incorrectly target the highly proliferative cells, while allowing survival of treatment-refractory tumor-initiating
“cancer stem” cells. These findings modify our conceptual approach to oncogenesis, and have dramatic
implications for breast cancer prevention, treatment, and drug development.
Although the existence of tumor-initiating “cancer stem cells” is gradually being accepted, the identification and
purification of tumor-initiating cells is still a great challenge due to the lack of specific markers that identify such
cells uniquely. Current methods rely on cell surface markers (e.g. CD44+/D24low/- (human) or CD29 High/D24High
(mouse) or fluorogenic enzyme substrates (e.g. the aldehyde dehydrogenase (ALDH) substrate Aldefluor –
Human only)) coupled with fluorescence-activated cell sorting or magnetic bead separation techniques.
Unfortunately, while these markers do allow enrichment of TIC, they do not allow purification to the degree
necessary for detailed analysis of TIC gene expression or function.
In an attempt to circumvent this problem, we are constructing a series of novel lentivirus-based fluorescent
signaling reporter vectors, including reporters for known stem cell regulators such as Wnt, Notch, and Hedgehog.
Crosstalk among these three pathways occurs frequently in normal development, and we suspect that this
crosstalk is important for TIC biology. In a recent review, Hayward et al. suggest that Wnt and Notch (‘Wntch’)
signaling are integrated such that rather than defining the fate of a cell, they determine the probability that a cell
will adopt a particular fate [51]. There is extensive evidence that the Wnt pathway can induce the expression of
Notch ligands as well as hedgehog signaling components, however, other interactions have also been reported
including antagonistic relationships. All three of these pathways have been shown to induce
epithelial-to-mesenchymal transition, a process essential for normal development and implicated in cancer
progression, and metastasis, as well as in the acquisition of stem cell characteristics [52].
Based on the preliminary results presented below, we anticipate that these reporters will be more effective than
current markers for purification of TIC, either alone or in combination. If results continue to be promising, these
individual signaling reporters can be incorporated into a single lentiviral vector that will allow us to monitor the
status of all three signaling networks in a single cell, and will also allow us to study the spatial location of TIC
within tumors in real time using in vivo imaging. Finally, because TIC may show a unique signaling “signature”
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with respect to the status of these three signaling reporters, we should be able to purify TIC more effectively and
evaluate their response to treatment using gene expression analysis methods such as microarrays and
proteomics.
In this proposal, we seek to use newly developed experimental and imaging methodologies to identify, localize,
and purify TIC. This will then allow us to identify and image TIC in vivo, and to model TIC behavior during tumor
development with respect not only to spatial localization and movement, but also with respect to specific changes
in gene expression and cellular signaling. Combined functional genomics and data mining strategies will allow us
to characterize novel growth regulators. Further, our combined experimental and systems biology approach will
allow us to evaluate responses to experimental therapeutics that may inhibit or kill TIC specifically in a manner
not possible before. Aside from a wealth of basic biological insight, future extensions of this work may allow drug
repositioning as well as development of directed, mechanism-based and “stem cell”-centric drug screening and
evaluation methods.
N4.1.C Preliminary Results
The first aim of Component 1 is to identify tumor-initiating cells (cancer stem cells) using newly developed
lentiviral fluorescent signaling reporters and to characterize their spatial distribution and behaviors during tumor
growth using in vivo microscopic imaging.
Characterization of canonical Wnt pathway using lentiviral transduction into primary tumor cells. The
Wnt/ß-catenin cascade has emerged as a key regulator of stem cell biology in multiple tissues, including the
mammary gland [53]. In addition, data suggest deregulated Wnt/ß-catenin pathway activity leads to uncontrolled
self-renewal of cancer cells and to resistance radiation therapy [54]. These observations suggest that TICs
might be characterized by increased canonical Wnt signaling. As a proof of principle, using a p53 null mammary
tumor, GFP positive cells derived by transduction with the TOP-eGFP lentivirus (a canonical Wnt-pathway
reporter) showed a marked enrichment for tumor initiating ability. Thus far, tumors have not formed from GFP
negative cells. In agreement with these data, FACS analysis has demonstrated a marked overlap of this cell
subpopulation
with
the
TIC
population
characterized previously as CD29H/CD24H.
Taking advantage of the eGFP fluorescence, we
can also visualize the location of these breast TICs
relative to tumor margins, blood vessels, and other
stromal cell types, in histological sections (Figure
11) and using in vivo confocal microscopy. Spatial
localization of TIC has not been possible prior to
these studies. Thus, this technique provides the
unique
opportunity
to
study
the
local
microenvironment or TIC niche. We plan to extend
these experiments initially to representatives of
each of the p53 null mouse mammary tumor
subtypes (luminal, basal-like and claudin-low) to
determine if canonical Wnt signaling is uniformly
activated in each case, as well as to our set of novel
human xenograft models representing basal,
HER2+, and ER+ breast cancers.
Figure 11. TOP-eGFP expressing cells (TIC-enriched)
(green arrow) are uniformly localized adjacent to blood
vessels (outlined in white, vasculature stained red for Von
Willebrand’s Factor).
Notch and Hedgehog signaling reporters are
available. In addition to the Wnt signaling reporter,
we have obtained Notch and Hh pathway reporters and are in the process of constructing and validating lentiviral
vectors for their ability to identify and localize TIC. Once validated individually, we will construct a triple pathway
reporter that can be used in different tumor models both from genetically engineered mouse models and human
breast cancer xenografts.
p53-null mouse mammary cancer models are available. Loss- or gain-of-function phenotypes associated
with mutations of p53 are observed in 20-30% of spontaneous human breast cancers [55], and the TP53
mutation status showed strong association with the basal-like and HER2-enriched subtypes where up to 50% of
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these ER-negative tumors may have TP53 mutations. Although deletion of both alleles of p53 rarely occurs in
human breast cancer, the relevance of the p53-null mouse model is that absence of p53 sensitizes the mammary
epithelium to other stochastic changes, and three main subtypes of p53 null tumors (i.e. luminal, basal-like and
claudin-low).
In collaboration with Dr. Daniel Medina at BCM, the Rosen laboratory has collected fifty p53 null mammary
tumors that develop stochastically at an average of 6 months of age following transplantation of p53 null
mammary epithelial cells (MECs) into the cleared mammary fat pad of wildtype p53 syngeneic Balb/c mice [56].
These tumors are aneuploid, and show varied histologies and biomarker expression including some estrogen
receptor(ER) positive tumors, and as described above, tumors of at least three different expression subtypes.
Early passage, stably transplantable, human xenograft models are available. The Lewis and Chang
laboratories have established a relatively large series of stably transplantable human breast cancer xenograft
models that accurately reflect tumor biology observed in the patient. To date, we have established 13 stably
transplantable xenograft models, including nine “triple negative”, two HER2+, and two ER+ breast cancer models
(one of which is also an inflammatory breast cancer). All of these novel xenograft models are available for our
use.
With respect to our Aim 1.2, to identify candidate genes and pathways that may regulate TIC behaviors (e.g.
self-renewal, differentiation, and metastasis, we isolated cellular subpopulations enriched for TIC from both
mouse (detailed in section N3) and human models and evaluated these enriched populations in comparative
microarray analysis in order to characterize candidate genes and pathways regulating TIC behavior.
Gene expression analysis of enriched TIC populations in human breast cancers.
We
(Lewis/Chang/Rosen) and others [57, 58] have developed “cancer stem cell signatures” from human clinical
samples. However, each of these studies has taken a unique approach for purification of TIC and generation of
the candidate gene list. In our effort to define a gene expression signature of tumorigenic breast cancer cells, we
obtained populations enriched for tumorigenic cells by two methods: CD44+/CD24-/low and the ability to form
mammospheres (MS). Comparative gene expression analysis was performed in populations enriched for
tumorigenic cells (CD44+/CD24-/low or MS) vs. non-tumorigenic cells (“other” flow sorted, or bulk tumor,
respectively), and then analyzed to there was significant overlap between both enrichment methods.
In the first comparison (consisting of 14 CD44+/CD24-/low vs. 15 “other” profiles, representing 19 patients and 9
patient pairs), 2221 RNA transcripts (1424 named genes) were elevated (p<0.01 unpaired, two-sided t-test; fold
change>1.5; FDR~0.2) in the flow-sorted
CD44+/CD24-/low vs. other cells; in the second
comparison (consisting of 15 MS vs. 11 primary
cancer profiles, representing 16 patients and 10
patient pairs), 2696 transcripts (1890 genes) were
elevated (p<0.01 unpaired, two-sided t-test; fold
change>1.5; FDR~0.25) in the MSs vs. primary
cancers.
The numbers of genes arising in the two separate
“enrichment” comparisons greatly exceeded
chance expected by multiple testing. The shared
gene overlap of 154 transcripts (117 genes)
between these two tumor-initiating enrichment
methods was significant (p=1E-5, one-sided
Fisher’s exact test) (Figure 12A). Between the
transcripts with decreased expression, 339
transcripts (263 genes) significantly overlapped
(p=1E-15, one-sided Fisher’s exact) (Figure 12B).
Thus, we defined a “CD44+/CD24-/low−MS gene
signature” which comprised the relative “up” and
“down” patterns of the 493 (154 over-expressed
and 339 under-expressed) transcripts present in
the significant overlap between both comparisons
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Figure 12. Generation of a gene expression signature from
the overlap of expression patterns of two cell populations
enriched for tumor initiating cell types.
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Program Director/Principal Investigator (Last, First, Middle):
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(heat map in Figure 12C).
Gene expression patterns characteristic of populations enriched for TIC are enhanced after hormone
and chemotherapy.
Tumor Volume (mm 3)
Tumor Volume (mm 3)
As detailed in section N3
above,
we
showed
previously that residual
tumors after treatment
with standard systemic
chemotherapies
are
enriched
for
cells
expressing cell surface
markers characteristic of
Figure 13. Enrichment of the TIC-enriched gene expression signature after either
TIC (CD44+;CD24-) and
hormone therapy (A) or chemotherapy (B).
further that these residual
cell populations are enriched for cells with mammosphere forming potential (a surrogate assay for TIC function).
We reasoned that if the frequency of TIC increased after systemic therapies, genes differentially expressed in
such cells relative to other cells in the tumor should also show enhanced correlation after treatment vs prior to
treatment. We have since tested this
Tumor Volume Xenograft 2665
Tumor Volume Xenograft 2147
1000.0
1400.0
hypothesis explicitly and have shown
1200.0
800.0
that TIC gene expression correlates
1000.0
better not only after conventional
600.0
800.0
chemotherapies, but also after hormone
600.0
400.0
400.0
therapy (Letrozole). These data suggest
200.0
200.0
that cells underlying tumor formation in
0.0
0.0
d0
d2
d6
d9 d13 d15 d21
both hormone receptor positive and
d0
d2
d6
d9 d13 d16 d20
Days
Days
hormone receptor negative subtypes
Chemo
Chemo
may share some underlying similarities
Merck003
Merck003
biologically and thus, targeting these
Vehicle Control (n=8)
Vehicle Control (n=9)
residual cancer cells is likely to be the
Docetaxel (10mg/kg, n=9)
Docetaxel (10mg/kg) (n=8)
Merck003 (100mg/kg) (n=8)
Merck003 (100mg/kg, n=8)
key to effective treatment of multiple
COMBINED (n=8)
COMBINED (n=9)
breast cancer subtypes.
Figure 14. Gamma secretase inhibition does not alter xenograft growth
n
)
C
om
bi
n
at
io
g/
kg
g)
(1
00
m
10
m
el
(
er
ck
00
3
ax
oc
et
D
M
om
C
g/
k
cl
e
Ve
hi
n
bi
n
at
io
g/
kg
g)
10
m
el
(
er
ck
00
3
M
oc
et
ax
D
(1
00
m
g/
k
cl
e
Ve
hi
PHS 398/2590 (Rev. 11/07)
)
MSFE (%)
MSFE (%)
With respect to Aim 3, to conduct a
significantly in two “triple negative” human breast cancer xenografts
“Directed Iterative Functional Genomic
(xenografts 2147 (left) and 2665 (right)). Note, both xenografts are
Screen” (DIFGS) to characterize genes
completely resistant to low dose chemotherapy (10mg/kg).
functionally that either decrease
tumor-initiating capacity or increase tumor-initiating capacity, we have ongoing functional genomics experiments
investigating the function of genes
Mammosphere Forming Efficiency (MSFE) 2665
Mammosphere Forming Efficiency (MSFE) 2147
in our “first generation” human TIC
0.4
0.4
a
signature, we are testing the effect
a
b
0.3
0.3
of lentiviral shRNA knockdown of
*a
genes comprising a “TIC signature”
0.2
0.2
a,b
*
on TIC behaviors. Results from an
0.1
0.1
initial screen of ~550 genes (1290
0.0
shRNAs) in SUM159 cells has
0.0
identified 101 genes represented by
116 unique shRNA lentiviruses that
regulate mammosphere initiation
significantly (increase or decrease),
many of which are known stem cell
Figure 15. Gamma secretase inhibition decreases the proportion of
regulators (e.g. beta-catenin and
mammosphere-initiating cells in treated xenografts in vivo.
Wnt5A in the Wnt/B-catenin
pathway, Hes1 a target of the Notch pathway, and hedgehog signaling components Ptch1 and SuFu). In this
project, we propose to extend these analyses in a directed manner by using sequential bioinformatic
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Wong, Stephen, T.C., Ph.D., P.E.
identification of all known and suspected interacting proteins, upstream regulators, and downstream targets of a
given set of experimentally validated TIC regulators such that new sets of candidate shRNAs can be screened in
an iterative manner until all known interacting genes are evaluated functionally in a set of tumor models. A certain
number of randomly selected “unknowns” (~50) can be included to identify novel regulators with each iteration of
the screen.
Mammosphere Forming Efficiency(%)
With respect to Aim 4, to define the
GANT61 Treatment
cellular responses of TIC to genetic and
Mammosphere Forming Efficiency
Xenograft 2147
pharmacological manipulation of genes
regulating TIC survival or function, in
0.15
Vehicle Control
addition to our functional genomics studies,
Docetaxel(20mg/kg)
we and others are actively investigating
GANT61(50mg/kg)
0.10
pharmacological inhibitors of known
COMBINED
cell-cell signaling and other regulatory
pathways governing tumor-initiating cell
0.05
survival, behavior, and treatment response
(e.g. Notch, Hedgehog, Wnt, PI3K/Akt) as
experimental therapeutics. Several of these
0.00
compounds, notably gamma-secretase
Figure 16. Hedgehog signaling inhibition by GANT61 prevents
inhibitors (Notch signaling) and Hedgehog
chemotherapy-induced enrichment of mammopshere-initiating cells.
signaling inhibitors, are showing promising
results not necessarily by reducing tumor volume (Figures 14 and 15), but by either reducing tumor-initiating cell
frequency as estimated by mammosphere-formation assay.
We are particularly excited by recent preliminary data using a novel class of hedgehog signaling inhibitor. In
this study, a stably transplantable smoothened-overexpressing tumor (T505) was transplanted into the cleared
#4 mammary fat pads of 36 FVB mice (JAX). When tumor reached a volume of 100-400mm3 mice were
randomized into 4 treatment groups: vehicle control, Docetaxel (20mg/kg), Hedgehog antagonist (40mg/kg), and
combination of Docetaxel and Hedgehog antagonist. Docetaxel was administered IP on Day 1 and Hedgehog
antagonist was administered by oral gavage daily for 10 days. On day 10 tumors were harvested, pooled by
experimental group, and dissociated to single cell suspensions. Cell suspensions were retransplanted bilaterally
into cleared mouse mammary fat pads at two cell concentrations to evaluate changes in the proportion of
tumor-initiating cells directly.
Hedgehog antagonist in combination with Docetaxel resulted in statistically significant reduction in tumor volume
relative to vehicle or either agent alone. Upon retransplantation of treated tumor cells, the vehicle group showed
3 tumors out of 6 fat pads (1,200 cells) whereas in Hedgehog antagonist, Docetaxel, and combination groups no
tumors formed at this concentration, with only a single tumor forming in the hedgehog antagonist group at 500
cells/fat pads.
In this proof-of-principle experiment, Hedgehog signaling inhibition, either alone or in combination with
chemotherapy showed efficacy similar to
chemotherapy alone in reducing or Table 2. Tumor formation rate in in vivo treated cell populations.
Tumor formation rate
eliminating tumor initiating cells in vivo
1200 cells/fat pad
500 cells/fat pad
using this particular model. Together,
3/6
1/6
these data are consistent with the Vehicle
Hedgehog
antagonist
0/6
1/6
hypothesis that hedgehog signaling
Docetaxel
0/6
0/6
inhibitors may be useful therapeutic
Combination
0/6
0/6
agents for breast cancer treatment by
specifically targeting the TIC population.
Section N4.1.D: Research Plan
N4.1.D.1. Aim 1.1: To identify tumor-initiating cells (cancer stem cells) using newly developed lentiviral
fluorescent signaling reporters and to characterize their spatial distribution and behaviors during tumor
growth using in vivo imaging.
Rationale. Currently available markers for TIC, including ALDH activity (Aldefluor assay) and cell surface
markers CD44, CD24, and CD29, show insufficient specificity to allow purification of TIC. Thus, localization of
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TIC is currently not possible, and definitive molecular analyses of TIC gene expression and function is not
efficient, and can, in fact, be misleading if a given manipulation functions at the level of the niche or
microenvironment rather than at the level of the TIC themselves. Fluorescent reporters for known and suspected
TIC regulatory networks should allow us to overcome these obstacles and allow real-time evaluation of
experimental therapeutics on TIC function and behavior. Spatial localization data can then be incorporated into
mathematical models and computer simulations of cancer development in Component 2.
Experimental design and methods.
Generation of pathway reporters: We have already established the utility of using the TOP-EGFP pathway
reporter to evaluate the importance of canonical Wnt signaling in the TIC subpopulation from several basal-like
mammary tumors. The frequency of the TOP-EGFPhi cells detected by FACS varies among the basal-like tumors
studied to date, however, expression of the EGFP reporter always correlates with TIC function. Importantly, no
TOP-EGFP cells were detected in preliminary studies in three of the claudin-low tumors. This suggests that
alternative pathways might be important in the claudin-low tumors. The Notch signaling pathway plays a critical
role in regulating mammary luminal cell fate commitment [59], so it will also be critical to use Notch reporters in
these studies. Finally, studies in our laboratories have also established a role for Smoothened in normal
mammary gland development suggestive of a role for Hh signaling in mammary gland cell fate determination
[60]. Thus, we will use established individual pathway reporters and develop a novel triple reporter for
simultaneously monitoring all three pathways known to play a role in stem cell self-renewal and differentiation.
For monitoring Wnt pathway activation, we obtained a TOP-EGFP lentiviral construct that contains a series of
three LEF-1/TCF binding sites and a TATA box. Wnt ligands bind to Frizzled receptors and trigger signaling by a
chain of events. This leads to the release of beta-catenin from a destruction complex, allowing it to enter the
nucleus and function as a transcriptional co-activator with TCF/Lef family transcription factors to drive
transcription of Wnt targets. This construct was the kind gift of Dr. Irving Weissman at Stanford University.
To assay activation of the Notch pathway we will use either pSIN-Hes1p-d4Venus or pSIN-TP-1-d4Venus-1[61].
The pSIN-Hes1p-d4Venus construct contains the 195 bp promoter region of Hes1, a direct target of activated
Notch signaling, driving a VENUS reporter. The pSIN-TP-1-d4Venus construct has an artificial promoter that
contains 12 RBP-J binding sites and a minimal promoter that drives the VENUS reporter. The intracellular
domain of Notch (NICD) acts as a membrane bound transcription factor. Once released following interaction with
its ligands, NICD translocates to the nucleus where it interacts with CBF1/RBP-J to drive transcription of target
genes. These constructs were the kind gift of Dr. Hideyuki Okano.
To assay activation of hedgehog signaling, we will subclone an 8xGli binding site promoter element [62] into a
lentiviral vector upstream of a tetrameric dTomato reporter [63]. A control vector containing an 8x mutant Gli
binding site will be used as a negative control for reporter specificity. Hedgehog ligands bind to Ptch receptors
(Ptch1 or Ptch2) relieving inhibition of Smoothened. Activation of Smoothened leads ultimately to production of
transcriptional activator forms of one or more Gli transcription factors (Gli1, Gli2, or Gli3). These activator forms
then translocate to the nucleus and activate target gene transcription [64]. These promoter elements were
obtained from the ATCC and have been validated in transient transfection assays in our laboratory.
After these initial vectors have been developed and validated, we will begin to construct other signaling reporters
as candidate regulatory networks are identified in Component 2. These may include reporters for STAT3
activation (cytokine signaling), TGF-beta signaling, PPAR-gamma etc.
We will test these reporters in a series of transplantable p53 null mouse mammary tumors representative of three
major breast cancer subtypes (ER+, triple negative, claudin-low). We have successfully adapted a protocol from
Dr. Bryan Welm, a former graduate student in Dr. Rosen’s laboratory, for infecting mammary epithelial cells in
suspension for use with dissociated tumor cells [65]. We have been successful in using this technology for
overexpression, gene knockdown, and reporters and transplanting the cells into the cleared mammary fat pad of
3 week old recipient mice to regenerate the normal gland as well as tumors. We are now optimizing a series of
doxycycline-regulatable lentiviral vectors for use in vivo in transduced mammary epithelial cells and in our
genetically engineered breast cancer models, and comparing the efficiency of vectors driven by elongation factor
 EF1with vectors driven by the spleen focus forming virus promoter/enhancer(SFFV) [66], as well as the
ubiquitin C(UBC) promoter.
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As mentioned above, there exists extensive cross-talk between these pathways during development. One
hypothesis is that if one of these pathways is inhibited therapeutically another may be induced as a
compensatory mechanism. In order to monitor all three pathways simultaneously in a given single cell, and test
this hypothesis, we will develop a triple pathway reporter system. We will clone each of the three regulatory
elements in front of a different fluorescent reporter. The three fluorescent reporters we will use are VENUS,
tandem dimer (td)TOMATO [67], and Enhanced Blue Fluorescent Protein (EBFP) [68]. VENUS and tdTOMATO
are both bright, have good spectra, and have proven to be non-toxic. For the third color, we will use EBFP in the
shorter wavelength range. The emission spectra for these three reporters do not overlap significantly and are
easily distinguishable using standard filter cubes or spectrally tunable electronic filters such as those on our
confocal microscopes. Some of these fluorescent reporters are being obtained from Dr. Timm Schroeder. We will
test these reporters in combination to determine if we can accurately detect two at a time and then all three
simultaneously by FACS. Baylor College of Medicine has a new state of the art Cytometry and Cell Sorting Core
(CTIC) facility. The CTIC instrumentation includes two FACSAria machines that can detect up to 13 colors plus
forward and side scatter and perform 4 way sorting. Activity of these reporters can also be evaluated and imaged
using confocal microscopy available both in the Integrated Microscopy Core and in the Lester and Sue Smith
Breast Center here at Baylor College of Medicine.
Potential pitfalls and experimental alternatives. It is possible that the EBFP reporter may not be bright
enough to detect low level activity of a given regulatory network. If this appears to be the case, we will replace the
EBFP with a brighter fluorescent protein reporter. In addition, reporters in the far red range may be preferable
under certain circumstances. However, far red fluorescence is not generally visible and thus is a bit more
technically challenging to use than a visible reporter since all localization must be done via the microscope
imaging equipment rather than by eye.
The rationale for creating the triple reporter is to ensure that all three reporters are transduced together into the
same cell. Introducing three pathway reporters into a single lentiviral vector is challenging and if unsuccessful,
alternatively, we will use individual reporters (or dual reporters) with different fluorescent markers that can be
co-transduced. In addition to the proposed uses of this reporter system in tumor studies, this resource will be
very useful for studies of mammary gland development and other tissues that can be reconstituted. It can also be
used for genetic and chemical screens in cell lines looking for activators or inhibitors of these pathways including
cross-regulation between pathways. These studies should help determine the functional differences or
similarities in the TIC subpopulations and provide the basis for more detailed gene expression and functional
genomic analyses described below.
N4.1.D.2. Aim 1.2: To identify candidate genes and pathways that may regulate TIC behaviors, e.g.
self-renewal, differentiation, and metastasis
Rationale. Under our previously funded SPORE grant, we derived a gene expression signature for
tumor-derived cell populations enriched for TIC using cell surface markers CD44+;CD24-/low. In this project, we
propose to extend these analyses considerably. A similar, but currently unfunded analysis was performed using
CD29H/CD24H for a small set of three p53 null mouse tumors. However, since these populations are only
enriched for TIC, identification of regulatory pathways critical for TIC survival and growth are not easily identified
using gene expression approaches. While our initial shRNA screen (described below in Specific Aim 3) based on
our human TIC-enriched signature yielded 101 candidate regulatory genes, we believe that specific signaling
reporters will enhance our ability to identify bona fide regulators more efficiently and will enhance our subsequent
“hit rate” in functional genomic analysis.
Based on our preliminary results indicating that a TOP-EGFP Wnt reporter allows enhanced identification and
further enrichment of the TIC subpopulation, we propose to use a combination of fluorescent signaling reporters
for signaling to purify TIC more efficiently and analyze their gene expression patterns more effectively. Genes
and pathways identified will be tested functionally in Specific Aim 3. These genes expression data can be
incorporated into developmental models to be developed by Component 2.
Experimental design and methods. TIC expressing one or more of the signaling reports used in Specific Aim
1.1 will be purified by FACS. The method for both human and mouse chip experiments is virtually identical except
for the Affymetrix chip used.
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RNA isolation, cDNA synthesis, and combined in vitro transcription and biotinylation labeling (IVT) on the purified
samples will be carried out according to protocols recommended by Affymetrix GeneChip TM (Santa Clara, CA).
Briefly, total RNA will be isolated using TRIzol (Invitrogen Corporation, Carlsbad, CA), and subsequently passed
over a Qiagen RNeasy column (Qiagen, Valencia, CA) for control of small fragments that have been shown to
affect RT-reaction and hybridization quality. Based on the yield from resting lymphocytes, we expect cancer
stem cell populations to yield 2 to 6 micrograms of total RNA per 10,000 cells. After RNA recovery,
double-stranded cDNA will be synthesized by a chimeric oligonucleotide with an oligo-dT and a T7 RNA
polymerase promoter at a concentration of 100pm/µL. Reverse transcription will be carried out according to
protocols recommended by Affymetrix GeneChip, using commercially available buffers and proteins (Invitrogen
Corporation). Biotin labeling and approximately 250-fold linear amplification followed by phenol-chloroform
cleanup of the reverse-transcription reaction product will be carried out by in vitro transcription (Enzo Biochem,
New York, NY) over a reaction time of 8 hours. The labeled cRNA will be hybridized onto an Affymetrix U133 plus
2.0 GeneChip (MG430 2.0 GeneChip for mouse) following the recommended procedures for prehybridization,
hybridization, washing, and staining with streptavidin-phycoerythrin. Antibody amplification will be accomplished
using a biotin-linked anti-streptavidin antibody (Vector Laboratories, Burlingame, CA) with a goat-IgG blocking
antibody (Sigma, St. Louis, MO). A second application of streptavidin-phycoerythrin will be used subsequent to
additional wash steps. After automated staining and wash, the arrays will be scanned on an Affymetrix
GeneChip Scanner (Agilent, Palo Alto, CA) and quantified (Affymetrix, Santa Clara, CA). Low-level microarray
data analysis includes quality assessment, normalization, and expression modeling [69, 70]. We most often use
dChip[71] for quality control assessments and preliminary analyses, and then convert expression to RMA or
GCRMA estimates for analysis in more complex linear models with Splus ArrayAnalyzer©, Bioconductor[72], or
BRB Arraytools (http://linus.nci.nih.gov/BRB-ArrayTools.html). We have been highly successful in the use of
gene expression arrays for identification of pathways responsible for chemotherapy sensitivity and resistance [1,
73].
Initial quality control and statistical analysis to identify candidate signaling pathways responsible for treatment
resistance, and for self-renewal of MS-initiating cells and putative breast cancer stem cells, will be conducted by
Dr. Susan Hilsenbeck and Dr. Chad Shaw. These data will also be provided to Component 2 for additional
bioinformatic analysis, i.e. identification of inhibitory compounds, drug repositioning, and mathematical modeling
of TIC signaling/regulation (please see below).
For the Affymetrix format, we will use MicroArraySuite® (Affymetrix) to generate probe-level quantitation data.
We will then use the DNA-Chip Analyzer (dChip) software package [71] to handle normalization, estimate
expression, and visualize results of other higher level statistical analyses. Expression data will be used for a
series of analysis of variance (ANOVA) calculations. These analyses will identify genes whose expression is
altered as a function of cell type and treatment. Resulting gene lists will be used in dChip to visualize the direction
and magnitude of expression changes. Functional annotations from the gene ontogeny database
(http://www.geneontology.org) can then be used to segregate known genes by the processes in which they
function (e.g. proliferation, differentiation, cell adhesion).
Low-level microarray data analysis includes quality assessment, normalization, and expression modeling [69,
70]. High-level analysis varies, depending on the questions to be asked, but will include clustering and gene-wise
selection using linear models. We will use pathway discovery software including Ingenuity and PathwayAssist
(Stratagene) [74] which uses natural language search algorithms to probe published literature for associations
between genes.
From the Affymetrix gene expression analyses, we will obtain at least three sets of genes: 1) genes differentially
expressed between Wnt responsive (EGFP+) vs. unresponsive cells; 2) genes differentially expressed between
Notch responsive cells (EBFP+) and all other cells; and 3) genes differentially expressed between Hedgehog
responsive cells (dtTomato+) and all other cells. There is the possibility that the different breast tumor
phenotypes (luminal, basal, ERBB2+) may show different patterns of expression in the profiling experiments, in
which case we could have more than three sets of genes to consider. We will determine whether the various
gene sets obtained from each of the expression profile datasets show a significant amount of overlap with each
other. Such an overlap would be indicative of nonrandom, biological associations between the different signaling
networks considered. A number of analytical methods would be used to look for patterns of enrichment, all of
which should yield significant results if true patterns exist in our data. A one-sided Fisher’s exact test would
compare the overlap between the top genes from each independent comparison over the chance expected
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overlap. Alternatively, rank-based approaches that help avoid using arbitrary cutoffs include Gene Set
Enrichment Analysis (GSEA) [75]
Validation by real time quantitative RT-PCR (QT-PCR) As with any technique, the results of expression
microarray analyses must be confirmed using an independent method. We will use real time quantitative PCR
(QT-PCR) for sampling a set of genes identified in microarray analysis with the highest likelihood of participating
in the phenotypic alterations. For this we will use TaqMan Low Density Arrays which, depending on the format
chosen, will offer quantitative analysis of up to 380 targets (+4 control targets) (“Format 384”) in a single
amplification run for an individual RNA sample. Since we are expecting approximately 500 named genes to be
altered significantly among the various gene sets, we will use the “Format 384” platform for our analyses. Each
sample used for microarray analysis will also be used with a low density array. Thus, approximately 60% of the
expected ~500 named genes identified by microarray can be validated for altered expression using this approach
using a single array per sample.
Proteomic analysis using Clontech antibody arrays. Antibody arrays are available from a number of companies
that now allow one to evaluate differential expression of ~500 human proteins (~300 for mouse)in a single
experiment using two color competitive binding and image analysis methods similar to those used for two color
analysis of mRNA expression. In this method, high-quality antibodies are immobilized in an array on glass slides.
These slides are then incubated with dye-labeled (e.g. Cy3) protein lysates as well as a dye-labeled protein
control (e.g. Cy5). A second array is also used in which the samples are labeled with the reciprocal dye
(dye-reversal). Differential fluorescence (Cy3/Cy5 and Cy5/Cy3) is then measured at each antibody spot location
on each and the two ratios are compared to generate an Internally Normalized Ratio (INR) value, which helps
correct for differential dye labeling and antibody/antigen binding.
We will use the Clontech Antibody Array 500 for human samples and Antibody Array 300 for mouse samples. For
the mouse tumors, we will evaluate three individual p53-null tumors representing each of three different tumor
subtypes (basal-like, ER+, and claudin-low). Cells will be separated using the previously identified CD29 and
CD24 cell surface markers into the CD29+/CD24+ population (the TIC-enriched population) and “all others”. For
the human tumors, we will evaluate four different triple-negative tumors as well as both of our HER2+ xenograft
models. Human tumors will be separated using the most effective marker identified above (e.g. ALDH, Wnt,
Notch, or HH activity) for the identification of the TIC fraction and sorted accordingly into the TIC-enriched
populations versus “all others”.
The AB Microarray 500 Slides (ClonTech, Mountain View, CA, USA) is a robust tool for high-throughput analysis
of proteomic profiles. This antibody array consists of 506 individual antibodies spotted in duplicate upon a glass
slide, and detects proteins in different functional categories including apoptosis, cancer, cell cycle, protein
kinases, and neurobiology. The antibody array protocol is a fluorescence-based procedure in which antibodies
printed on a glass surface are used to capture fluorescently-labeled antigens. The buffers in the Ab Microarray
Express Buffer Kit yield the highest signal to noise ratio and are specifically formulated to minimize background
binding. The mouse array is similar, but slightly smaller.
Protein extraction and labeling will be prepared according to the manufacturer's small-scale protocol and using
Protein Extraction & Labeling Kit (ClonTech, Mountain View, CA, USA). A common reference will be generated
by mixing all the samples together and this common reference will be labeled with Cy 3, and each sample will be
labeled with Cy 5 (GE Healthcare, Piscataway, NJ, USA) before being passed through a Microspin Desalting
Column (Pierce Biotechnology, Rockford, IL). The labeled common reference and sample pair will be applied to
a single slide, and standard protocol will be followed. The slides will be dried and scanned using an Axon
AL4200 scanner and GenePix 6.0 software (Molecular Devices, Sunnyvale, CA).
The scanned images will be analyzed with GenePix 6.0 software. A GPR result file with signal intensities with
and without background, signal to noise ratio (SNR) in each channel (Cy3 and Cy5), flag (to reflex the quality of
each spots) etc. will be produced for each array. Bioconductor will be used to process the GPR result files and
printTipLoess normalization will be performed. A ratio for each antibody spot on sample vs. common reference
will be calculated for each array. This ratio will be used for further statistical analyses across all the samples. We
will perform a two sample ttest for two group comparison, and an ANOVA for multi-group comparisons.
Differential expressed proteins will be identified and then validated using traditional methods such as Western
and/or ELISA.
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Expected results
We expect that cell populations enriched for cells capable of self-renewal will show similar sets of genes/proteins
that are differentially expressed relative to cell populations lacking self-renewal capacity. For example, Wnt
responsive cells may overlap considerably with Hedgehog responsive cells, if both populations contain the TIC. If
TIC are responsive to all three signaling pathways being examined (responsive to Wnt, Notch, and Hedgehog
signaling), triple positive cells may identify critical genes essential for TIC function more effectively than any other
method used to date.
Thus, from this Aim, we hope to identify active pathways responsible for therapy resistance and self-renewal. It
is hoped that by examining the gene expression patterns of cells that are chemoresistant and tumor-initiating, we
will be able to generate hypotheses concerning novel therapeutic targets. In addition, we predict that new
markers and reagents for “stem cell” isolation will result, and that some of these new markers will specifically
label “cancer stem cells”, providing targets to be used in producing new anti-cancer stem cell therapies.
Potential pitfalls and experimental alternatives: The techniques to be used are well established in our
laboratories and we have published extensively in this area. As such, we do not expect any technical problems.
It is possible that a given fluorescent reporter may not emit at a high enough level to be easily discriminated by
FACS (e.g EBFP). If this is the case, a more robust fluorescent protein reporter (dt) Tomato, Venus, EGFP) will
be chosen and cloned downstream of the target promoter.
In the event that no proteins are identified as differentially expressed in Clontech’s antibody arrays, we propose
to use a different antibody array platform— PanoramaTM Ab Microarray- XPRESS Profiler725 from Sigma. This
array contains 725 antibodies printed on a glass slide, and the proteins from Sigma’s array cover different
functional groups such as gene regulation, cell signaling, MAPK & PKC pathways, and p53 pathways.
Clontech’s and Sigma’s arrays have only 70 proteins overlapping, therefore, combining these two antibody array
platform, we will be able to analyze total 1160 proteins. At the present time, Clontech’s arrays showed better
quality with lower background and higher signal to noise ratio, therefore, we decide to use Clontech’s array in this
grant and Sigma’s array as a backup.
N4.1.D.3. Aim 1.3: To conduct a “Directed Iterative Functional Genomic Screen” (DIFGS) to characterize
genes functionally that either decrease tumor-initiating capacity or increase tumor-initiating capacity.
Rationale. We developed an initial gene expression signature of cell populations enriched for TIC consisting of
456 differentially expressed genes, and conducted a functional genomics shRNA screen of these genes (and
selected others) to determine which of these genes are important for the regulation of mammosphere formation
(a surrogate assay for TIC function). From this initial screen, we have identified ~101 genes that either increase
or decrease mammosphere formation when disrupted. It is our goal to now extend these functional analyses
considerably to conduct a genome-wide screen in a directed, iterative manner.
We hypothesize that direct binding partners, as well as immediate upstream regulators and downstream targets
of this subset of genes will be highly enriched for additional genes regulating TIC function. Further, that our
functional genomics screen can be conducted iteratively such that the entire library of shRNAs can be screened
in a directed fashion.
Experimental design and methods. Starting with our validated list of 101 genes, we will use bioinformatics
tools, including Ingenuity Pathway Analsysis (IPA) and KEGG repositiory diagrams, as well as our own
knowledge of individual regulatory pathways to generate a new candidate gene list again consisting of
approximately 500 genes. In our preliminary work, about 1300 shRNAs can be screened in a single experiment
with relative ease and efficiency. Larger numbers of shRNA may be feasible to screen with modification of our
automation methods but we are not anticipating being able to conduct a full genome analysis in a single
experiment.
For the DIFGS, lentiviral shRNA sublibraries will be generated in a sequential manner and used to transduce
SUM159 and Hs578T cells as we have performed previously. Significant changes in mammosphere formation
efficiency will be evaluated by plating 2000 transduced cells per well in each well of 96 well ultra-low attachment
plates and MSFE evaluated after 3-5 days using GelCount colony imager and quantification software. The
experiment will be repeated eight times for the entire sublibrary in each cell line.
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Mammosphere formation efficiency will be normalized within each plate relative to the median
mammosphere-formation efficiency and ranked by Z-score. Z-scores across all 8 replicates will be averaged,
re-ranked, and given a p-value. A p-value of 0.05 or less will be considered significant. All shRNA achieving a
significant p-value on either cell line will be rescreened against each cell line to confirm an effect on
mammosphere formation efficiency.
Finally, the function of each shRNA on TIC will be confirmed using one or more xenograft lines as the source of
cells for transduction and transplantation, as funding permits
Expected results. Based on our initial screen of 1290 shRNA representing ~500 genes, we expect
approximately 100 genes will show a significant effect on mammosphere formation in one or the other cell lines
for each set of 500 genes chosen. We further expect that our “hit rate” will increase using a directed iterative
approach thereafter because there should be a functional link already known among the candidates chosen. We
expect there to be a significant overlap in the genes showing functional effects on mammosphere formation
between the two cell lines chosen because these two cell lines are similar to one another being both of the
basal/claudin-low subtype.
Experimental pitfalls and experimental alternatives. Given that we have performed this type of screen
already, we do not anticipate technical problems. However, it remains possible that a reduction in mammosphere
formation may not accurately reflect a reduction in TIC in all cases. Thus, the in vivo validation of each and every
hit via transplantation of transduced cells is critical. We will conduct these in vivo validations as funding and time
permits.
N4.1.D.4. Aim 1.4: To define the cellular responses of TIC to genetic and pharmacological manipulation
of genes regulating TIC survival or function in vivo.
Rationale. In addition to cell surface markers in use currently in our laboratories, we will extend these studies to
use the Wnt, Notch and Hh specific pathway reporters to allow us to assess the activation status of these
pathways in TIC from the different subtypes, and determine how pathway activity and TIC localization/mobility
changes in response to systemic and “stem cell targeted” therapies.
Experimental Design and Methods. In addition to standard systemic chemotherapeutics (Docetaxel 20mg/kg
or Doxorubicin (5mg/kg)) and radiation (2Gy), we will also use experimental therapeutics under development for
inhibition of various pathways thought to play a role in tumor-initiating cell proliferation, self-renewal, and survival
etc. For each pathway, we will treat mice with single agent biologics with and without combination with
chemotherapy, and again measure bulk tumor volume, measure long term survival, and measure effects on TIC
numbers using both mammosphere assays as a surrogate assay for TIC function, as well as transplantation to
define definitively the effect on TIC number in vivo. In addition, gene expression changes in purified cell
populations will be monitored and evaluated using microarray and proteomic strategies in order to inform our
developmental and cell cell signaling models.
Many developmental pathways, such as Wnt, are challenging to target. While small molecule Wnt pathway
inhibitors are under development in several laboratories, these are not yet available for animal studies. As soon
as these are available they will integrated into our studies. However, we can test the effect of disrupted Wnt
signaling using a genetic approach by lentiviral overexpression of either an inhibitory shRNA (identified already
in our initial shRNA knockdown screen in Specific Aim 3 above) or a dominant-negative ß–engrailed construct
used previously in the Rosen laboratory to inhibit canonical Wnt signaling [76] .
For the Notch pathway, we will give a gamma-secretase inhibitor (MK003) that is currently under study in our
laboratory. In addition, we have two alternatives and are testing both in vivo. One is GSI-X from CalBiochem, and
the other is called DBZ and this one is showing effects on intestinal cell behavior and looks promising [77]; the
DBZ compound is given at a dose of 0.048 mg/day/mouse, with a 3 day on and 4 day off schedule, and then
given this way, it promotes goblet cell formation in the gut, which has been shown to be a hallmark of Notch
pathway inhibition [78].
Finally, inhibitors of the Hedgehog pathway include those targeting Smoothened, specifically cyclopamine (a
generous gift from Infinity pharmaceuticals), CUR0199691 (Genentech/Curis) (under study and with which we
have extensive experience [79]), LDE225 (Novartis)(pending permission), and IPI926 (Infinity
Pharmaceuticals)(pending permission), as well as an agent targeting the Gli1/Gli2 transcription factors called
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GANT61 [80]. All hedgehog inhibitors are currently under study using human xenograft models representing the
basal subtype, and are available in Dr. Lewis’ laboratory in quantities sufficient for in vivo experiments proposed
herein. Since human xenograft models representing the claudin-low subtype are not yet available, the mouse
claudin-low tumors established in the Rosen laboratory will now allow us to test these agents for their ability to
target TICs in this subtype.
In addition to these three major tumor-initiating cell regulators, we will also extend our studies to other
developmental signaling pathways as they are identified, or as relatively specific inhibitors become available.
Candidate inhibitors include those for STAT3 (cytokine and growth factor signaling targeted by an inhibitor
developed in the laboratory of our collaborator Dr. David Tweardy), and c-Src/Fyn kinases (implicated in our
initial shRNA screen above and targeted by Dasatinib (BMS)), as well those against as c-Met (HGF receptor),
TGF-beta signaling, and PPAR-gamma function. Both our STAT3 inhibitor and Dasatinib are showing promise in
vitro and in vivo.
Expected results. Based on our preliminary data, we expect inhibitors of Wnt, Notch, and Hedgehog to all have
significant effects to reduce the frequency of TIC in vivo. With the exception of the GSI, which is known to have
GI toxicity with extended use, we do not anticipate significant side effects due to treatment over the timeframe
used since no adverse side effects have been noted thus far. We anticipate that one or more of the additional
inhibitors to be studied will have similar effects on the TIC population in limiting dilution transplantation
experiments.
Potential pitfalls and experimental alternatives. It is likely that one or more of the signaling inhibitors chosen
does not function at the level of the TIC, but rather functions at the level of the TIC microenvironment or niche
(e.g. blood vessel formation, fibroblasts, macrophages etc.). We do not see this as a significant problem since
we will be able to dissect these behaviors in vivo using our imaging and signaling reporter approaches. In fact, if
one can target both the TIC and the niche in combination therapies without the need for chemotherapies, one
may be able to avoid the inherent adverse side effects chemotherapies induce. For some regulatory pathways
identified, small molecule inhibitors may not be available. In such instances, we will attempt to use an inducible
shRNA expression lentivirus in which an inhibitory shRNA can be expressed under the control of a
doxycyclin-inducible promoter which is functioning in vitro, and is being tested in vivo currently.
N4.2 Component 2 – Computational Biology: Mathematical Modeling and Computer Simulation
Title: Analyze and Model TIC Tissue Microenvironment
Project Leaders (core PIs): Xiaobo Zhou, Ph.D., Vittorio Cristini, Ph.D.
Project Summary:
Mathematical modeling involves the use of mathematical equations and relationships to represent biological
phenomena. Complementary to this type of modeling is the use of computer simulations to represent these
modeling approaches in multiple dimensions. These approaches serve two purposes. First, they provide a basic
framework for the interrogation and integration of data, often providing insight into the type and quality needed for
addressing a hypothesis or experimental design. This feature is especially useful when trying to integrate or
analyze the large datasets generally associated with systems biology. Second, and more importantly, these
models or simulations should allow one to predict the biological state under investigation and predict how the
natural process will behave in various circumstances.
These problems center on the understanding of the behavior of biological systems whose function is
governed by the spatial and temporal ordering of multiple interacting components at the molecular, cellular, and
tissue levels. We will also develop bioinformatics and bioimaging models to integrate and analyze the data
generated from Component 1, and make use of the information obtained from data analysis, biological
knowledge to build in silico models to model TIC behavior, cancer cell apoptosis, cell migration, cell cycle
changes and drug treatment response. The goal of this component is to take advantage of our combined
expertise in cell biology and computational modeling to develop coherent experimental protocols and construct
biomathematical models for understanding the mechanism underlying breast cancer stem cell evolution, i.e.,
how one stem cell evolves into breast tumor with various sizes and compositions in cell microenvironment. Our
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hypothesis is that that TIC behavior during tumor development can be simulated using a robust, multiscale
mathematical/computational model of TIC behavior during breast cancer development. Further, that these
models can be built to reflect not only the molecular, cellular, and tissue-level dynamics, but also to allow
prediction of the response of TIC to experimental therapeutics.
Key Personnel (listing percent of effort of each individual);
Stephen T.C. Wong, Ph.D., P.E. (center PI), 20% in Component 2 (5% in Administrative Core, 5%
education core); Xiaobo Zhou, Ph.D. (Core PI), 30%; Vittorio Cristini, Ph.D. (Core PI), 10%; Ching Tung,
Ph.D., (co-investigator), 5%; Jeff (Chung-Che) Chang, M.D., Ph.D., (co-investigator), 10%; John Baxter,
M.D., (co-investigator), 5% ; David Engler, Ph.D. (co-investigator), 5% ; Paul Macklin, PhD. (co-investigator)
17%; Xiaofeng Xia, Ph.D, (co-investigator), 30%;
Hong Zhao, M.D., Ph.D (Research Associate), 100%, Fuhai Li, Ph.D. (Postdoc) 100%, Di Huang, Ph.D
(Postdoc) 50%, Xiaorong Yang, Ph.D. (Postdoc) 100%, Guangxu Jin, Ph.D. (Postdoc), 100%, Xiuwei Zhu,
M.Sc., (Research Programmer) 100%, Huiming Peng, M.Sc., (Research Programmer) 100%
N4.2.A Specific Aims
Component 2 is guided by the hypothesis that TIC behavior during tumor development can be simulated
using a robust, multiparameter mathematical/computational model of TIC behavior during breast cancer
development. Further, that these models can be built to reflect not only the molecular, cellular, and tissue-level
dynamics, but also to allow prediction of the response of TIC to experimental therapeutics. Thus, the central goal
of Component 2 is to build a multi-scale model platform of TIC mE for investigating TIC self-renewal,
proliferation, localization, and other functions within a spatially and molecularly-regulated microenvironment.
Based on the experimental data obtained from Component 1 and published knowledge of TIC, we will model the
TIC tissue microenvironment (TIC mE) from molecular level, cellular level up to tissue level. The TIC mE model
can further predict and guide the pathway analysis, the candidate gene selection, genetic and pharmacological
manipulation in Component 1. Accordingly, the Specific Aims of Component 2 are:
Aim 2.1: To model the TIC tissue mE mathematically based on 2D and 3D microscopy and image
analysis
The microenvironment, including cellular and non-cellular components, is well-known to play an important role in
supporting and influencing the behavior of TIC. Image bioinformatics models will be developed to quantify the
TIC tissue microenvironment images obtained from Component 1 and then TIC mE spatial distribution can be
modeled. Based on the quantified data as well as from the literature and online databases, we can apply ordinary
differential equations (ODEs) and more sophisticated differential equations to describe the relationship among
TIC and molecules, enzymes, nutrients and other cell types in the microenvironment (e.g. fibroblasts,
vasculature, immune cells). This mathematical model in an effort to model tumor development in silico. This will
be a model at the cellular and tissue levels; however, the molecular level mechanisms should be more basic and
important for understanding the biological problems, therefore, further experiments will be carried out in Specific
Aim 1.2 of Component 1 based on the feedback of the results obtained in Aim 2.1.
Aim 2.2: To predict the TIC pathways or key genes related to specific cancer so to refine TIC
microenvironment model
Bioinformatic analysis of DNA microarray and proteomic data generated in Specific Aim 1.2 of Component 1,
coupled with the genetic and pharmacologic manipulations of TIC function in Aims 1.3 and 1.4, will enable us to
identify key candidate components in the pathways that are related to cellular behavior and survival.
Subsequently, we will map these signaling pathway factors to specific tumor cell types and further to specific
cellular properties by modeling them as functions of the factors. For example, psy  f ( x1 ,..., xn ), pasy  g ( x1,..., xn ) ,
where x1 ,..., xn are genes/factors, and f and g are the functions that model the relationship between symmetric
or asymmetric rates and the genes in TIC pathways. The TIC mE model will, in turn, be refined based on the
newly inferred pathway and network information. With the network of genes integrated into the biomathematical
model, predictions can be made by changing the parameter values for the network components, and then a
subset of key factors will be found. These predictions will guide the iterative functional genomics experiments in
Aim 3 of Component 1 to focus on the most likely gene candidates.
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Aim 2.3: To develop image bioinformatics models for mapping gene functional networks within and
among TIC and niche cells from the directed iterative shRNA screen and further refine the TIC mE model
We will develop image bioinformatics models for discovering gene functional networks by integrating gene
function annotation results from shRNA genome subset screening in Specific Aim 1.3 and publicly available
multi-modality genomic data. We will first develop an integrated image analysis system for shRNA screens and
score each gene based on the phenotypic information, then we will develop an image-based system biology
approach to study the gene functional networks. Biological processes are often an orchestra of groups of genes,
and the gene functional network studies are important to understand and study gene functions. Combining with
the prior knowledge, the gene functional annotation results from shRNA whole-genome study will have the
potential to identify known or suspected interacting proteins, immediate upstream regulators, and downstream
targets. Compared with the predictions of refined TIC mE model in Aim 2.2, new experimental data that are
unanticipated by the model can be used to further improve our mathematical TIC mE model.
Aim 2.4: To model the response of TIC and their microenvironment to genetic and pharmacological
manipulations of TIC function in vivo
Based on our ability to assay the relationship between exposure to signaling inhibitors and gene expression in
relatively pure cell populations, as well as the mathematical model linking molecular level data to the cellular and
tissue levels, we can adjust the model to predict the response of TIC to new drug candidates. Technologies will
be designed to elucidate, interrogate, and model the role of physical forces on varying cellular functions,
including cellular ligand-receptor interaction, cell proliferation, differentiation, cell cycle evaluation, apoptosis and
evolution of tumor phenotypes, or motility in order to facilitate an increased understanding of the role that
physical forces play in cancer pathology and metastasis. Under different conditions, e.g., metastasis or
non-metastasis stage, increased or decreased motility, changes in intracellular mechanics and ability of cells to
interact with the environment will all be included for modeling the distribution of tumor-initiating cells. The
collaboration of the Aim 2.4 and Aim 1.4 will be in an iterative manner to better refine the mathematical model in
order to derive more robust drug candidates for inhibiting or managing TIC.
N4.2.B Background and Significance
N4.2.B.1 Research on TIC
Cancer is the second most common cause of death in the United States, surpassed only by heart disease.
According to the American Cancer Society, death from cancer is not only due to the wide occurrence in nearly all
of human tissues (e.g. breast, brain, colon, head and neck, lung, pancreas, and prostate), but also due to the
extreme difficulty in curing cancer. The range of experimental data available is expanding dramatically such that
it is rapidly exceeding our ability to conceive an intuitive understanding of the underlying mechanisms. Therefore,
bioinformatics analysis and mathematical modeling is necessary to analyze and manage data and provide
informative and intuitive interpretation. Indeed, mathematical modeling and computer simulation are tools that
can provide a robust framework to understand cancer progression better. Through a collaborative effort involving
experts in mathematical modeling and experimental biologists who understand that modeling and experiments
are complementary, we will expand our knowledge of cancer more efficiently and effectively.
The mechanisms underlying the cancer initiation, expansion and progression are extremely complicated.
Besides the genetic abnormalities that may or may not be present, the effects of microenvironment for cells are
also of great importance. The interactions between tumor cells and their regulatory factors (both cellular and
non-cellular) in the microenvironment are obviously complex. It is clear that cell-cell signaling mechanisms, as
well as the nutrient and oxygen concentrations may vary spatially and temporally, because of the distribution of
blood vessels and the different cell types. Indeed, it is these variations in cell densities and nutrient and oxygen
concentrations that create the 'unique microenvironment' that is responsible for the growth or inhibition of
cancers. While a large amount of data has recently been generated in cancer biology, it constitutes only
'component knowledge’ of the 'system knowledge' for cancer. The integration of experiments designed on in vitro
cell culture and in vivo animal model, and integrated with mathematical methods on data analysis and prediction
is deem necessary, this will be our goal in this proposal.
Tumors have been found to be heterogeneous both structurally and functionally, with TIC as the key
component. TICs have been isolated from many types of tumors, including the breast cancer. It has been
gradually accepted that, like the normal stem cells, TICs in breast cancer also act as the engine for tumor
initiation, expansion, evolution, migration, and response to therapy as well [81]. It is though that the proliferation
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and differentiation of TICs significantly affect tumor growth dynamics and heterogeneity. Meanwhile, recent
evidence shows that many pathways are involved in regulating the cellular behavior of TIC [82], including Wnt,
Notch, Hedgehog, EGFR, and so on. Currently, it is widely accepted that TIC self-renewal patterns are at least
partially dependent on the specific microenvironment, or niche, which is a particular growth environment
consisting of different cell types and extra-cellular matrix components [83-85]. Therefore, the properties of TICs
can be better characterized by including information from the molecular, cellular, and tissue levels, which is a
major goal of our proposal.
Breast cancer cells can be separated conceptually into three cellular subpopulations (though intermediate
subtypes might exist): tumor-initiating cells, progenitor cells and “differentiated” cells, each of which can be
present in different proportions within a given tumor. Resembling the normal stem cells, TICs divide
symmetrically to grow TIC population and divide asymmetrically to produce progenitor cells, which in turn divide
to generate “differentiated” tumor cells (cells contributing to the bulk of the tumor, and perhaps
division-competent, but with no tumor-initiating potential). As these tumor cells divide, the tumor volume
increases and the nutrients and oxygen are consumed. When the nutrients and oxygen concentrations fall below
a certain level, tumor cells necroses and some growth factors (e.g. VEGF) are secreted, and then the
angiogenesis will occur due to the gradient of growth factors. After this stage tumor cells will continue to
proliferate and even invade normal tissues. Finally, metastasis occurs through blood vessels and/or lymph
vessels. To understand the mechanisms underlying breast cancer, signaling pathway networks and their
regulating functions on tumor cell properties will be investigated as well.
Clearly, to devise effective clinical treatments, a sufficient understanding of the breast cancer from
molecular to cellular and tissue level is required. We believe that basic and clinical research efficiency would be
greatly enhanced through a complementary approach that couples computational modeling together with a
targeted experimental program. Throughout this proposal, the following key questions in development of breast
cancer will be considered: (1) how do tumor cells distribute in the breast tissue, especially the TICs? (2) How do
TICs behave in the tumor microenvironment? (3) How does the angiogenesis occur and what is the relationship
between vasculature and TICs? (4) What other signaling factors can lead to tumor growth and metastasis? (5)
How are drugs delivered into the tumor microenvironment and what is the responses of tumor cells? (6) What
multiple drug strategies can be employed to efficiently treat the breast cancer?
Bearing these questions in our mind, we propose to 1) develop computational models that are grounded in
experimental evidence, involving multiple linkages between tumor cells and their microenvironment; 2) make
predictions and then guide experiments by using the proposed models and 3) refine both our proposed model
and experiments by iterative improvement in response to feedback between them. If successful, we will have a
framework to simulate the progression of breast cancer, which can be used to analyze and interpret experimental
data, and most importantly, we will have developed a predictive tool that can guide the design of new
experiments. By this means, experiments can be motivated by quantitative "engineering hypotheses" rather than
qualitative hypotheses or intuition alone. Close collaboration between experts in breast cancer biology and
engineering will quantitate known signaling mechanisms, and allow identification of new signaling mechanisms
between tumor cells and cells of the microenvironment. Various state-of-the-art mathematical models applied to
different spatial and temporal scales will be developed in conjunction with a series of experimental studies in
order to improve our understanding of breast cancer.
N4.2.B.2 Drug response modeling
It is thought that the breast cancer is a heterogeneous three-dimensional composite of fibrous and
connective tissues, stromal components, vasculature and multiple clones of cancer cells. Cancer drug therapy,
which generally can be classified as either radiotherapeutic or chemotherapeutic (targeting tumor cells) or
antiangiogenic (targeting vascular endothelial cells), can help control the growth of tumor lesions by impairing
cell division or triggering apoptosis in tumor cells, or by inducing apoptosis in endothelial cells. However, the
heterogeneity and three-dimensionality of the tumor environment presents a challenge to drug assessment, both
during development and in the clinic, consequently hindering development of effective therapies. Mathematical
modeling and computer simulation are tools that can provide a robust framework to understand cancer
progression and response to drug treatment. Also, successful therapeutic agents must overcome biological
barriers occurring at multiple space and time scales and still reach targets at sufficient concentrations. A
computer simulator founded on the integration of experimental data and mathematical models can provide
valuable insights into these processes and establish a technology platform for analyzing the effectiveness of
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drug treatments with the potential to cost-effectively and efficiently screen drug candidates during the
drug-development process.
In particular, bio-computational modeling of tumor drug response has endeavored in the last two decades to
address this need [42, 49, 86]. Doxorubicin cellular pharmacodynamics has been modeled [87]. Drug transport
was modeled in spheroids versus monolayers [88]. A model capable of predicting intracellular doxorubicin
accumulation that matched experimental observations was described [89]. Different drug kinetic effects in vitro
were compared [90]. Models using multi-scale approaches (i.e., linking events at sub-cellular, cellular, and tumor
scales [91]; studying vascularized tumor treatment [92]; and simulating nanoparticle effects [93]) have also been
developed. Recently, a novel multi-scale computational model [46] extended a previous formulation of tumor
growth founded in cancer biology [44, 47] to enable more rigorous quantification of diffusion effects on tumor
drug response. This model can represent nonsymmetrical solid tumor morphologies three-dimensionally, thus
providing the capability to capture the physical complexity and heterogeneity of the cancer microenvironment.
This kind of integrative method, tightly coupling computational modeling with biological data, enhances the value
of knowledge gained from current pharmacokinetic measurements. It further shows that such an approach could
predict resistance based on specific tumor properties and thus improve treatment outcome.
As conceptualized, tumor-initiating cells (TIC) may be responsible for tumor growth, maintenance, resistance
to treatment and disease relapse. If this hypothesis is
right, it stands to reason that building TIC into the drug
response model as a component dependent on the
other tumor cells (TC) and other elements of the mE is
a valid approach. The spatial distribution of TIC will be
different from TC as tumor growth and accurately TIC
will more close to the vasculature system because the
viability of TIC is stronger than TC. Combining with
Figure 17. Schematic representation for the composition
another fact that vasculature is an important factor on
in a tumor, including the niche (the grey region in the
drug response, the novel multi-scale model in our
dashed rectangle) for tumor initiating cells.
proposal will have biological relevance.
N4.2.C. Preliminary Results
The CSMCaD investigation team has worked together in tumor modeling over the past two years. We will
introduce the preliminary results about the TIC tissue microenvironment image analysis and modeling in Section
N4.2.C.1. Preliminary results in pathway analysis will be presented in Section N4.2.C.2. Cellular image analysis
and RNAi whole genome screening will be presented in Section N4.2.C.3, and drug response modeling in
Section N4.2.C.4.
N4.2.C.1: TIC Tissue Microenvironment Modeling
N4.2.C.1.1 Tumor population dynamics and stem cell niche
Experimental and clinical researchers have recently found that breast cancer can approximately be
separated into three cellular subpopulations, which are stem-like cells, progenitor cells, and differentiated cells.
Therefore, three compartments are used in our model: cancer stem cells which produce progenitor cells, which in
turn generate general tumor cells. Dynamics of these three types of cell populations are described by
mechanisms shown in Figure 17. Although it
dNTIC
may not be the exact mechanism underlying
 Psy  TIC  NTIC  d1  NTIC
(1)
breast cancer, we assume that in our model:
dt
TICs divide either symmetrically to form two
dN PC
   Pasy  TIC  NTIC  PC  N PC  d 2  N PC
(2)
TICs (k1) or asymmetrically to form a TIC and a
dt
progenitor cell (k2). Progenitor cells divide
dNTC
symmetrically to form two progenitor cells (k 3)
 PC  N PC  d3  NTC
(3)
with a short-term division capacity, and then
dt
lose their proliferation capacity and produce
TCs (k4) via differentiation; TCs do not divide, but can be lost from the system through many processes, e.g.
apoptosis. The death rates for TICs, PCs and TCs are denoted by d1, d2 and d3 as shown in Figure 17.
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Taken these together, the dynamics of the three cellular populations can be described by equestions (1)-(3),
where NTIC , N PC and NTC are cell numbers of these three population; Psy and Pasy are the probabilities for
symmetric and asymmetric divisions of TICs. As we assume TICs only divide in these two ways, thus we have
Psy  Pasy  1 . TIC and PC are division rates for TIC and PC. The parameter  in equation (2) is the expansion
scale for PC, which are related to the proliferation capacity of PC. For simplicity, we use the function of   2 Ndiv
to fit this process; here N div is the maximal number of division.
Considering the function of cancer stem cells that are related to the niche, we have three hypotheses as
follows: First, TICs can only reside in their niches where they obtain the necessary nutrients and molecules to
survive and maintain their stemness. Once leaving the niche, they will differentiate into progenitor cells and
subsequently into terminal differentiated cells. Second, niche is a specific anatomic location for stem cell
populations, and thus we assume that the niche has a limited volume, namely carrying capacity, for cancer stem
cells. As a consequence, only a limited number of cancer stem cells can exist. Third, the probability of division
pattern, i.e. symmetric or asymmetric, is decided by the ratio of cancer stem cells to carrying capacity. As the
niche is occupied by more and more cancer stem cells, asymmetric division is more likely to undergo.
Therefore, we can integrate the effect of niche into the system by describing the probability of symmetric
division as the function of TICs in the niche. Similarly to Roeder et al [94], we model the relationship between Psy
and NTIC by a general class of sigmoid functions: Psy ( NTIC )  f ( NTIC )   v1  v2  exp(v3  NTIC / N niche )   v4 ,
1
where v1, v2, v3 and v4 determine the shape of Psy ; N niche
is the maximal number of TICs that the niche can hold.
N4.2.C.1.2. Receptor-ligand interactions
In recent years, methods have been developed for
modeling various signaling pathways. These methods
can now be adapted to virtually any signaling pathway as
long as a sufficient number of pathway components are
known. As an example of this, we have modeled the
EGF-EGFR pathway with respect to the TIC mE. Many
studies suggest a significant correlation between EGFR
signaling pathway and cellular proliferation and
survival[95-98]. Following the work of Eladdadi et.al
(2008), we consider the proliferation rates and apoptosis
rates as functions of the numbers of receptors complex
of EGF-EGFR (Figure 18). It is worth noting that while
the epidermal growth factor receptor family composes
Figure 18. A schematic representation of the effect of
four types, we just use EGFR to represent the whole
conventional treatment and novel treatment on tumor
behavior through the EGF-EGFR pathway.
family for simplification. Indeed, our case can be seen as
the special case in Eladdadi’s work, by setting the
parameters related to EGFR and HER2 with identical values.
Normally, the combination of EGF to extra-cellular domain of EGFR causes the activation of the receptors
and leads to a series of interactions between activated receptors, recruited proteins, and plasma membrane
molecules eventually activate the multiple downstream effectors, which are implicated in the control of
proliferation and survival[98]. However, the presence of
other ligands or molecules, such as lapatinib, which

 [ EGF : EGFR]eff
EE
i   act ,effi  max,i
(4)
function at the intracellular domain to block tyrosine kinase
half  [ EGF : EGFR]eff
activity, thus inhibiting the autophosphorylation of
d max,i
receptors and receptor dimerization, blocks the activation
EE
di   repeff,i 
(5)
of downstream pathways that are responsible for
1  [ EGF : EGFR]eff / kd
proliferation and apoptosis (e.g PI3K/Akt) [99-101].
Although the receptor number varies in different cell lines or cell types due to differential gene expression,
we assume that in our model, the total receptor number (denoted by EGFRt) is a constant in the a given cell type,
which will be used as a first approximation here. Considering the reactions that are related to EGF-EGFR, two
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simple
chemical
reactions
are
Wong, Stephen, T.C., Ph.D., P.E.
described:
EGF  EGFR
kf
EGF : EGFR
and
kr
EGF : EGFR  TKI
kb
EGF : EFGR : TKI . Where kf, kr, kb and ku are rate constants. Since the chemical reaction
ku
rate is much faster than cellular behavior such as cell proliferation, we use the quasi-steady state to obtain the
concentration of EGF-EGFR. Using the law of Michaelis-Menten kinetics, we derive the concentrations of
EGF-EGFR and EGF-EGFR-TKI as follows:
[ EGF : EGFR] 
[ EGFRt ][ EGF ]
[ EGF : EGFR][TKI ]
and [ EGF : EGFR : TKI ] 
, where Km1 and Km2 are
K m1  [ EGF ]
K m 2  [TKI ]
Michaelis constants. Therefore, the effective EGF-EGFR complex that can activate downstream factors is:
[ EGF : EGFR]eff  [ EGF : EGFR]  [ EGF : EGFR : TKI ] .
N4.2.C.1.3. Mathematical description of cell proliferation and apoptosis
Let us now consider how to link the cell proliferation rate and apoptosis with the effective EGF-EGFR.
Similarly to Monod [102] and Eladdadi et.al.[97], we use the Michaelis-Menten kinetics to model the saturated
effects of cell proliferation rate with respect to the EGF-EGFR concentration as in equation (4), where max is the
maximum cell proliferation rate,  half is the number of occupied receptors required to generate a half-maximal
response. Similarly, the repression function of apoptosis (death rate) is simply modeled as equation (5), where
dmax is the maximum death rate and kd is a constant for repression threshold.
Substituting equations (4)-(5) into equations (1)-(3) of the dynamics of tumor populations, we can obtain a
new modeling system that incorporates ligand-receptor reactions and stem cell niche into the cellular dynamics
of tumor, as in shown in equations
dNTIC
EE
EE
(6)-(8).
 Psy ( NTIC )   act ,effTIC  NTIC   repeff,TIC  NTIC
(6)
dt
N4.2.C.1.4.
Parameter
value
dN PC
EE
EE
EE
selection
   Pasy   act ,effTIC  NTIC   act ,effPC  N PC   repeff, PC  N PC
(7)
dt
The parameters used in this model
dNTC
EE
EE
are listed in Table 3, most of which are
  act ,effPC  N PC   repeff,TC  NTC
(8)
dt
based on recent experimental data or
scientific literature. The maximal death
rates for these three populations were derived from Michor et al’s work[103]. The maximal division rate for PC
cells was estimated using the doubling time of HB4a cell lines t1/2=48hours:  max,2 =ln(2)/t1/2=0.0143hour-1; no
data was available for estimation of max,1 ,
we assumed that max,1 =0.5*  max,2 due to
the findings that TIC divides slower than
PC cells. Number of receptor complexes
required to generate a half-maximal
response  half was adopted from
Table 3. Parameters used in our mathematical model. MH2005
represents the paper of (Michor, Hughes et al. 2005); EI2008
represents the paper of (Eladdadi and Isaacson 2008); HO2003
represents the paper of (Hendriks, et al. 2003)
Eladdadi et al’s work. K m1  kr / k f was
calculated using the data from Hendriks
et al; we assumed the same value for
K m 2 . The initial concentration of EGF
ligand was obtained from Hendriks et al
and TKI concentration was set initially
with 1.0*10-9M. The total number of
receptors per cell was simply the
summation of EGFR and HER2 from
experimental studies, which varies from
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210000 (normal) to 800000 (high HER2 expression level)[97].
N4.2.C.1.5. Simulation results
The effect of EGFRt on tumor dynamics: As described previously, the total number of EGFR per cell
varies in a large range due to different cell lines, however, the relationship between total EGFR and tumor growth
cannot be intuitively obtained.
Therefore, we simulated the tumor
volumes over time by changing the
parameter value for the total EGFR
in our model from 200000 to
800000. The simulation results are
shown in Figure 19, from which we
can see that the tumor grows faster
and the final volume of the tumor is
also larger as the total EGFR
increases, as shown in Figure 19A.
In addition to the growth advantage
Figure 19 simulated tumor growth with various total EGFR per cell. (A)
prediction, we note that our model
compares the cell population growth. (B) shows the relationships between
also predicts that the dose-response
total EFGR numbers and tumor growth data (points taken from A at time=1500
of tumor to total EGFR approaches
and time=1600).
a saturation function as the total
EGFR increases. As a result,
increasing the level of total EGFR beyond a maximum number (threshold) will not alter the tumor growth rates
and final volumes. As shown in Figure 19B, the dose response dependence of cell proliferation rates on total
EGFR becomes weaker (a smaller slope).
Tumor response to various treatments: Let us now consider the tumor response to different treatments.
Without treatment, the tumor reaches its steady stage after a certain time, with a constant volume and TIC
percentage.
Conventional
treatments will be simulated by
increasing death rates of PC
and TC by an additional 50%.
Our simulation results in
Figure
20
show
that
conventional treatment results
in a decrease of tumor volume,
and
an
increased
TIC
percentage (Figure 20B), as
observed experimentally in our
preclinical and clinical studies.
For the treatments with TKIs
Figure 20. Simulated response of tumor to different treatments. (A) shows tumor
(e.g. lapatinib), the initial
volumes changes over time and (B) shows TIC percentage changes over time
concentration of TKI was
without treatment (solid line), with conventional treatment (green dotted line) and
1.0*10-9M.
The
effective
with the treatments of TKIs. Note, while red dashed lines represent treatments with
EGF-EGFR
complex
is
TKI that inhibit proliferation and induce apoptosis, the blue dash-dotted lines shows
decreased due to the binding
treatments including the change of self-renewal patterns as well.
of TKIs to the intracellular
domain of EGFR. While we assumed that the amount of effective EFG-EGFR is related to increased proliferation
rate and decreased apoptosis, the presence of TKIs simultaneously represses the proliferation rate and induces
the death rate of TIC, PC and TC cells. While the repression of tumor volume is obvious as shown in Figure 20A,
the increased TIC percentage is not expected. Indeed, Li et al. found that the treatment with lapatinib (a kind of
TKIs) led to a non-statistically significant decrease in the percentage of CD44+/CD24-/low cells that are thought to
be cancer stem cells[3]. In our explanation the TKIs not only have effects on the proliferation rate and apoptosis
rate, but also have effects on changing the self-renewal patterns, that is, shifting symmetric division to
asymmetric division, in other words, inducing the differentiation of the TICs.
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Therefore, in addition to inhibiting the effective EGF-EGFR, we also multiplied Psy ( NCSC ) with a value of 0.25,
which is again a first approximation and will be modeled with a continuous function in future, to model the shift of
TIC division from a symmetric to an asymmetric pattern. The simulation results are shown in Figure 20 with blue
dash-dotted lines. This time, not only decrease of
tumor volume but also the slight decrease of TIC
percentage is observed when the TKI treatment is
imposed, which is in agreement with the finding of Li
et.al[3]. Furthermore, it is worth noting that even
after treatment stops, the tumor volume still
decreases for a certain time, and a sharp increase of
TIC percentage occurs after the treatment. This can
be understood in the following way: when the
treatment stops, the tumor volume is small and TIC
cells shift back to symmetric division, therefore, an
increase of TIC population and a decrease of PC
and TC continue, resulting in a repression of total
volume of tumor but an increase of TIC percentage.
N4.2.C.2. Pathway Inference in regulating TIC
cells
Mapping the key signaling molecules in
Figure 21. The signal transduction mapping for TICs.
biochemical pathways will be central to pathway
study drug discovery efforts [104-107]. However,
study of signal transduction currently faces two key challenges: First, the pathways themselves are far from
being completely constructed. For example, the signaling pathway map for the various subtypes of breast cancer
is still not available. Second, key pathway components suspected of participating in disease will have to be
validated as potential drug targets. This might explain the high failure rate in search of completely new targets
and in repositioning of old drugs. Therefore, it is critical for drug discovery to construct a signal transduction map
that has the ability to reveal correlation between drug targets and pathways of interests with a particular disease
in a cause-effect format.
Signal transduction from the outside to the inside of a cell is performed and completed by many diverse
molecules, such as hormones, growth factors, neurotransmitters, cytokines and cell adhesion proteins and small
molecules. Protein-protein interactions (PPIs) play a key role in the biological process. It is an important strategy
in systems biology to construct signal transduction from PPIs[108, 109]. In our study, we filtered a protein-protein
interaction data for Mus musculus (Mouse), i.e. Filtered Protein Network (FPN), based on five published PPI
databases, that is, DIP [110], BIND [111], MIPS [112], MINT [113] and IntAct [114]. Based on Filtered PPIs and
available signaling pathways, we found that a special interacting pattern, i.e. network motif [115, 116], play a key
role in signal transduction of a cell. Network motifs are patterns that occur in different parts of a network at much
higher frequencies than those found in randomized networks and have been proposed as the essential
components in signal transductions. Through checking the properties in network topology, we found they are
clustering between cancer signaling pathways and other signaling pathways.
Microarray on mouse mammary tumor-initiating cells generated in the Rosen lab was used to construct the
essential signal transductions in TICs [4]. The cells were labeled (for FACS sorting) with CD29 and CD24
antibodies, and four subpopulations were collected (CD29HighCD24High, CD29HighCD24Low,CD29LowCD24High, and
CD29LowCD24Low) to do either in vivo transplantation or to isolate RNA from each of them for array to correlate the
in vivo data. Twelve samples (RNA of four subpopulations based on expression of CD29 and CD24 for each of
three tumors) were included in the identification of differentially expressed genes of tumor-initiating cells. Five
samples (RNA of subpopulations based on expression of CD29 and CD24 of normal mammary epithelial cells)
were included in the normal group analysis. A reference RNA was used to normalize all samples.
The fold-changes (FCs) of genes were computed by comparing the subpopulations of CD29HighCD24High with
other subpopulations. Four genes, Lsm5, Calm3, Bmi1 and Ezh2, were highly up-regulated in the
CD29HighCD24High subpopulations (FC > 4). Bmi1 plays an important role in regulating the self-renewal capacity
of hematopoietic, as well as human mammary gland stem cell. Two genes, Calm3 and Bmi1, are involved in
network motif clusters. Moreover, the differential genes (FC > 3 or FC < 0.25) are significantly enriched in ‘Cell
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Cycle’, ‘ Calcium’, ‘ Long-term Potentiation’, ‘Wnt’, ‘Phosphatidylinositol’, ‘Focal adhesion’, ‘MAPK’, ‘Adherens
junction’, ‘Cell adhesion molecules (CAMs)), ‘B cell receptor’, ‘T cell receptor’, ‘Adherens junction’, and ‘Tight
junction’ of KEGG (Kyoto Encyclopedia of Genes and Genomes) [117].
Construction of signal transduction mapping: The key signal transduction networks in TICs were
constructed using the Multiple Objective Optimization Model (MOOM). The inputs of MOOM are network motif
clusters surrounding Calm3 and Bmi1, FCs of differential genes, and enriched signaling pathways. The outputs
of MOOM are protein-paths with differential expressions of genes and cellular processes of signaling pathways.
The component proteins in the output protein-paths satisfy that (1) be most differentially expressed in Lin
—
CD29HCD24H cells and (2) pass through a large number of enriched signaling pathways. The output
protein-paths from the model satisfy that they dominate the most differential genes and pass through a large
number of the interested signaling pathways identified from differential genes. These output protein-paths can be
combined into a network by the shared common proteins, as shown in Figure 21. Most of these genes are either
up-regulated or down-regulated in the subpopulations of CD29HighCD24High. By using IPA (Ingenuity Pathway
Analysis) software, we identified that the up-regulated genes (in red) can accelerate several essential functions
in the survival and renewal of TICs, such as ‘Cell Growth and Proliferation,’ ‘Hematological System Development
and Function,’ ‘Cancer,’ ‘Cell Cycle,’ ‘Cell Signaling,’ ‘Cellular Assembly and Organization,’ and ‘Cellular
development,’ and the down-regulated genes (in green) can decelerate many functions related to
‘Post-translational Modification,’ ‘Cell Growth and Proliferation,’ ‘Cell-To-Cell Signaling and Interaction,’ ‘Cell
Signaling,’ and ‘Cell Death.’ The signaling transduction mapping is important in revealing the key molecules
involved in TICs and uncovering the molecule mechanisms for the survival or renewal of TICs.
N4.2.C.3. Cellular image analysis for the Directed Iterative Functional Genomics shRNA screen
Similar to colony-forming assays under anchorage-independent growth conditions in soft agar, we have used
mammosphere form efficiency (MSFE) as a surrogate for tumor-initiating and progenitor cell function and as a
measure of cells capable of self-renewal in breast cancer specimens. TICs and at least some progenitor cells are
able to survive and generate spheroid structures termed mammospheres (MS) by anchorage-independent
growth in suspension culture. Using malignant mammary epithelial cells, MS can be transplanted to form tumors
with the same cell type as the parental tumor.
3D Breast Cancer Mammosphere Segmentation: To quantitatively analyze the individual cells from the
3D mammophere, cell segmentation is necessary. In 3D cell segmentation, cell contact and intensity variation
M
E  o   I  ci
i 1 
2
1  H   dv    I  c 1  H    dv     g  I     dv      1  H   1  H   dv
M
2
i
b
b
i


 i
2

   i  o I  ci  b I  cb
t


2
M

j 1, j i
M
i
i 1 
H  i   g
i
M
i 1 j  i 1 
  i
 i
vgdiv 
 i
  i
i
j
M




1  H  i  


j 1, j  i



(9)
(10)
are two major challenges. In this proposal, we implement a segmentation method, which consists of cell center
detection and cell body segmentation, to deal with these two challenges. To detect the cell centers, the iterative
voting method proposed in [118] is employed. In summary, the iterative voting method uses oriented Gaussian
kernels, whose topography is refined and reoriented iteratively, to filter the images for detecting the cell centers.
Finally the cell centers can be detected by using simple thresholding method, e.g. Otsu’s method [119]. This
method has an excellent ability of noise immunity, and is shown to be tolerant to perturbation in scale. For details,
please refer to [118].
To separate the cells from background, we apply the Otsu threshold method in local regions. However, the
local adaptive threshold method cannot separate the clustered cells. The aim of cell center detection is to provide
‘seed’ information for the cell body segmentation to separate clustered cells. To segment the cell body, we
propose a modified multiple level set method. Each cell is represented using one level set function, and the
spheres around the detected cell centers with radius r (which is empirically set as the minimum radius of cells),
are used as initial contours. Specifically, in the proposed multiple level sets method, we integrated the intensity
information [120], the first and second terms in the energy function, and geodesic length information [121], the
third term in the energy function, together. In addition, we introduce the interactive energy, the fourth term in the
energy function, to prevent the adjacent level sets from overlapping. The energy function is provided in Equation
(9). The evolution equation for each i , as seen in Equation (10), is then obtained by deducing the associated
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Euler–Lagrange equation. where M denotes the number of cells;  is the image domain; ci is the average
intensity inside i-th cell, and cb is the average intensity of the background; H represents the Heaviside function,
and  is the Dirac function; g is the edge indicator function. The representative breast cancer images and
segmentation results are provided in Figure 22.
We will extend the 2D feature extraction in Section N.3. They include 3D Zernike descriptors, 3D Haralick
co-occurrence features, spherical harmonic features, regional features, and phenotype shape descriptors. The
(a) of 3D features are(b)
(c)
(d)
(e)features below.
last two types
extension of 2D features;
we briefly describe
the other three
Figure
22. A representative
breast
3D image.
(b) superior
two slices over
of mammal
(c), of
(d) noise
two
3D Zernike
descriptors:
2D cancer
Zernikemammosphere
moments were
found (a),
to be
otherssphere;
in terms
3D visualization
from different
view perspectives.
(e) 3D rendering
of the
segmented
cells.
sensitivity,
information
redundancy
and discrimination
power [122].
Guided
by this,
Canterakis [123] generalized
the classical 2D Zernike polynomials to 3D, however, in his work Canterakis considered mostly theoretical
aspects and 3D Zernike Moments, which are directly derived from 3D Zernike polynomials similar as in the 2D
case, are not invariant under rotations.
3D Haralick co-occurrence features: texture is one of the most commonly used features used to analyze
and interpret images by measuring the variation of the intensity of a surface and quantifying properties such as
smoothness, coarseness, and regularity. Although the traditional Haralick texture features were concentrated on
2D image, they have been extended to 3D volumetric data [124-126]. In 3D case, the Haralick features can be
calculated along the same way as in the 2D case. It is worth noting that the number of directions in the 3D case
is 13 instead of 4 in the 2D case during the computation of co-occurrence matrices.
Spherical harmonic features: spherical harmonic is an important property in many theoretical and practical
applications. Moreover, in 3D computer graphics, spherical harmonics plays a special role in a wide variety of
topics including indirect lighting and in recognition of 3D shapes. Herein, we propose a novel kind of feature
which is based on the rotation invariant spherical harmonic representation proposed in [127], called spherical
harmonic feature, to describe the shape of 3D tumor for our special research purpose.
Multidimensional drug profiling using Kullback-leibler divergence (KLD) the heterogeneous
morphological structures of cells indicate the influence of different drug treatments [128-130]. Using KLD metric
we can profile the drug effects compared with control. For each segmented cell image, we calculated 211
quantitative features [8, 131]. Given a quantitative feature, we obtained two populations: control and drug
treatment. Then the KLD metrics between control and treatments are calculated as follows. For each plate, we
collected the cells from control wells together as the pooled control population. For each replicate of the
treatment, we first generated a sub-population with the same size of the replicate by sampling with replacement
from the pooled control population, and then the KLD between the sub-population and the replicate is calculated
for each quantitative feature.
N4.2.C.4.Preliminary Results of Drug response modeling
We (Cristini and colleagues) use a multiscale computational model [46], extending a previous formulation of
tumor growth founded in cancer biology [40, 43, 44, 47, 48], to enable more rigorous quantification of diffusion
effects on tumor drug response. This model can represent nonsymmetrical solid tumor morphologies
three-dimensionally, thus providing the capability to capture the physical complexity and heterogeneity of the
cancer microenvironment. More significantly, we fully constrain the model through functional relationships with
parameters set from experiments. The hypothesis is that the simplest relationships that would at the same time
be biologically founded and which could be calibrated by the experimental data. These relationships link tumor
mass growth and regression to the underlying phenotype. We provide the mathematical basis for describing cell
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mitosis, apoptosis, and necrosis modulated by diffusion gradients of oxygen, nutrients, and cytotoxic drugs, and
enable quantification of the physiologic resistance introduced by these gradients. Input parameters include the
diffusion coefficients for these substances and the rate constants for proliferation, apoptosis, and necrosis. We
measure parameter values from independent experiments done under conditions with no gradients (i.e., with
cells grown as one-dimensional monolayers) and then use these values to calculate cell survival in
three-dimensional tumor geometry. These values are compared with experiments in which cells are grown as in
vitro tumor spheroids, representing a three-dimensional tumor environment with diffusion gradients.
This approach allows us to (a) fully constrain the computational model, using experimentally obtained
parameter values, and (b) validate the hypothesized functional
relationships by comparing the computed three-dimensional
tumor viability with the spheroid tumor growth experiments. By
quantifying the link between tumor growth and regression and
the underlying phenotype, the work presented here provides a
quantitative tool to study tumor drug response and treatment.
N4.2.D. Research Plan
In this component, we propose to develop biomathematical
modeling to model TIC tissue mE in Section 4.2.D.1, pathway
analysis and TIC mE remodeling in Section 4.2.D.2, image
bioinformatics for target discovery and TIC mR remodeling in
Section 4.2.D.3, and in-silico models to model TIC behavior and
drug treatment response in Section 4.2.D.4.
Figure 23. 3-D segmentation of breast cancer
cells
in a tissue section, cells are labeled with
D.4.2.D.1. Aim 2.1: TIC 3D Tissue Image Analysis and TIC
different
colors for clarity.
Tissue mE modeling
N4.2.D.1.1 TIC 3D Tissue Image Analysis
Stacks of confocal images will be used to reconstruct the spatial distribution of Wnt-, Notch-, and
Hedgehog-resonsive cells, which, at least in the case of Wnt-responsiveness, represent the TIC. Additional
reporters will be employed as they become available. We will first reconstruct the 3-D structure of TIC cell
populations separately. Each cell and blood vessel (or macrophage, fibroblast etc.) in the TIC tissue
microenvironment will be segmented and registered with a 3-D coordinate, so that the TIC population can be
obtained to study the 3-D distribution. The accurate detection and segmentation of 3-D cells and vessels is a
crucial prerequisite for this subtractive reconstruction. In spite of the great number of commercial image analysis
software available, cell cytoplasm detection and segmentation remain as challenging problems due to the
complicated cellular appearances like the irregular shape, touching cells, and intensity heterogeneity. We
(Wong and Zhou labs) have developed software packages for cellular image segmentation [8, 13, 113, 132-134],
and we were successful in using the algorithms we developed to derive 3-D segmentation scheme for breast
cancer cells (Figure 23). Armed with these algorithms, we seek to accomplish the 3-D subtractive reconstruction
that cannot be done using any current commercially available software. The bioimage analysis software we
developed will be freely available for the research community after publication.
The spatial distribution of the TIC cell population will be analyzed to see whether they are highly clustered. A
classical tool, Ripley’s K function will be used to analyze the spatial point pattern [135]. The definition of the K
function is as follows: K(t)=E(d<t)/ λ, where E(d<t) denotes the number of cells within a distance t of an arbitrary
cell, and λ the density of cells (mean number of cells per unit area). The density can be estimated by N/A, where
N is the observed number of points and A is the area of the field of view. Using Ripley’s K function test, we will be
able to determine whether TIC cells are distributed randomly or clustered in a breast tumor.
Stacks of confocal images will be used to reconstruct the tumor vasculature (or other cell types). Considering
the structure of vasculature, we will employ a curvilinear structure detector [136] to detect the center lines, and
then use center line based level-sets [136] to segment the cells, and finally get the reconstructed 3-D vasculature
by surface rendering. The shortest distance between a cell and the vascular surface will be calculated for each
tumor initiating cell. The distance distribution will be plotted and compared with the control group to determine
whether TIC cells are specifically concentrated in perivascular regions.
D.4.2.D.1.2 TIC tissue microenvironment (mE) modeling
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Angiogenesis and Blood Flow: We simulate tumor-induced angiogenesis in 3D using a hybrid
continuum-discrete, lattice-free random walk model, which refines earlier work by [137-140]. The model
generates the vasculature based on tumor angiogenic regulators, represented by a single variable representing
excess of pro-angiogenic regulators (e.g., VEGF). Endothelial cells (ECs) near the sprout tips proliferate and
migrate by chemotaxis and haptotaxis [137, 138]. We model non-Newtonian blood flow with hematocrit in the
neovasculature by adapting and refining the network flow equations in [43, 44] to our new, lattice-free
vasculature[47, 141]. Similarly to [142], we determine the vessel radius in each network segment by balancing
forces: vessel blood pressure (tied to the flow rate) and wall elasticity; and external tumor-generated stresses.
This dynamic coupling between external mechanical forces, vessel radius, and flow can then feed back into the
dif 
f usion
by v essels
uptake by all cell species
decay



 release

 


 

2
0  Dσ  σ  δ v esselν a, u b 1  σ   λσ ,U,V ρ V  λσ ,U,H ρH σ  λσ ,D σ
0  Dd  2 d  δ v esselν a, u b 1  d   λd ,U,V ρ V  λd ,U,H ρH d  λd ,D d
0  Dσ  2 n V  λnV ,S ρ V H σ H  σ   λnV ,D n V  δ v essel λnV ,U n V





 

release by hy poxic tumor cells
decay
(nutrient transport)
(drug transport)
(11)
(VEGF transport)
uptake by ECs
angiogenesis model by altering the branching probabilities[47, 141].
Transport of Nutrients, Drug, and Growth Factors: The vasculature releases nutrients (oxygen and
glucose) σ that diffuse through the tissue and are uptaken by cells during metabolism, while tumor cells secrete
VEGF (nV) in response to hypoxia. Drug d (e.g. erlotinib) extravasates from the microvasculature and diffuses
through the tissue. On the time scale of tumor proliferation (days), we can assume that the governing equations
are quasi-steady[40, 47, 143]. These assumptions are formulated as in Equation 11, where Dσ are diffusion
constants, δvessel (Dirac delta function) gives the microvasculature position, ν is the delivery rate (depends upon
avessel and ub), and λσ,U,i, λσ,D, and λnV,,S are the uptake, decay, and secretion rates. Also, H is the Heaviside
“switch” function, defined to be 1 where σH - σ > 0 (in hypoxic regions) and zero elsewhere.
Tumor Growth and Tissue Invasion: While the mathematical model is described in greater detail
elsewhere[39, 44, 47, 48], we provide an overview of and adapt our model to include treatment of tumor initiating
cells (TIC), progenitor cells (PC), and tumor cells (TC). Our general approach is to treat cells and tissue
according to classical physical conservation laws (making the model mechanistic) while incorporating the
appropriate biology as carefully-chosen constitutive relations among the physical variables.
3D Distribution of Cell Species: Tissue is modeled as a mixture of interstitial fluid, ECM, and various cell
species with densities ρV (viable tumor cells, including TIC, PC, and TC sub-populations), ρH (viable host), and ρD
(apoptotic and necrotic tumor and host cells). Cell-cell and cell-ECM mechanical interactions are modeled with a
flux J that generalizes Fick’s Law [44]. The rate of change in ρi (i = V, D, H) is determined by balancing cell
advection (·(uiρi), where ui is the velocity of the cell species), cell-cell and cell-ECM interactions (adhesion, cell
incompressibility, chemotaxis, and haptotaxis; incorporated in Ji), and net cell creation (Si: proliferation minus
ρi
   ui ρi  Ji   Si , for i  V, D,H
t
(12)
apoptosis and necrosis) [44, 47]. See Equation 12.
Proliferation, Apoptosis, and Necrosis (Si): Each species’ density ρi (i = V, D, H) increases through
proliferation and decreases by apoptosis and necrosis. For simplicity, we assume these primarily affect tumor
mass through the water transport in the tissue and hence neglect their solid fraction[48].
Cell-Cell Interactions (Ji): Tumor and host cells adhere to one another but preferentially adhere to like cells,
modeling (i) relatively low host cell density; (ii) degradation of the stroma by MMPs [144, 145]; and (iii) cell sorting
experiments showing differential adhesion [146]. Thus, the tumor and host interface is relatively well-delineated,
with interface thickness dependent upon the interaction of the forces given above. We express these effects with
a mechanical interaction potential function E that depends upon ρi; the precise form of E is in[48].
Cell Species Velocity (ui): The movement of cell species i is determined by the balance of
proliferation-generated pressure, adhesion, chemotaxis (due to substrate gradients), and haptotaxis (due to
gradients in the ECM density f). We model the motion of cells and interstitial fluid through the ECM as porous
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medium flow as a viscous, inertialess fluid [44].
Tumor TIC-PC-TC Dynamics (Tumor Composition): The tumor cell density ρV consists of TIC, PC, and TC,
which differ in cellular properties, such as proliferation rate, differentiation capacity, chemotaxis strength, and
cell-cell adhesion. In any time interval (t, t + Δt], TICs divide symmetrically into two identical daughter TICs with
probability PTIC→TIC, and asymmetrically into a TIC and PC with probability PTIC→PC. In turn, PCs divide
symmetrically into two daughter PCs with probability PPC→PC, and divide symmetrically into two daughter TCs
with probability PPC→TC. Tumor cells divide symmetrically into two TCs with probability PTC→TC. Each of these cell
types has a probability of
apoptosing Pi,A, where i = TIC, PC,
1 d  fTIC V 
 PTIC TIC  PTIC , A fTIC
(13)
TC. Once averaged within any fixed

dt
V
volume, we obtain the system of
1 d  f PC V 
Equations (13)-(15).
 2P
f  2P
P
P
f
(14)



TIC  PC TIC
PC  PC
PC TC

V
dt
Note that each of the transition
d
f
1  TC V 
probabilities above depends upon
 PPC TC f PC  PTC TC  PTC , A fTC
(15)
the microenvironment and genetic
V
dt
makeup of the cell constituents.
Furthermore, notice that the fixed transition probabilities for fixed time intervals used in many models (e.g. [147])
can be derived from the more general stochastic process detailed in [148].

PC , A
PC

Discrete Cell-Scale Agent Model: Each cell is modeled as a semi-deformable sphere with radius r, mass m,
distribution of surface adhesion receptors E and I (e-cadherin and integrins), position x, velocity v, phenotypic
state S (proliferating P, apoptosing A, migrating M, quiescent Q, or necrotic N), and classification C (TIC, PC, or
TC) (Figure 24).
Phenotypic Transitions: Each non-quiescent state (P, M, A, or N) has a fixed completion time (βP-1, βM-1, βA-1,
and βN-1, respectively). We assume the cell cannot leave P or M prior to completion, except for necrosis (N) or
apoptosis (A; during therapy). Transitions from Q to A and P are regulated by stochastic processes that depend
upon the microenvironment[149]. After proliferation is complete, the daughter cells’ classifications are assigned
according to the symmetric/asymmetric proliferation probabilities in the TIC-PC-TC dynamics above. Transitions
to the apoptotic and motile states are governed similarly, and cells become necrotic if σ < σH for a longer time
period than a fixed “survival” time βH-1.
Balance of Forces: The cell’s velocity is obtained by balancing the forces acting upon it: cell-cell adhesion and
repulsion, cell-basement membrane (BM) adhesion and repulsion, and the net force of migration balance with
interstitial fluid drag and cell-ECM adhesion by an “inertialess” assumption[43].
Motility: Effects of several signaling pathways will be
considered. For example, to approximate EGF-EGFR signaling
dynamics, we choose direction and strength of the motile force
randomly with a bias towards the maximum EGFR
activation[43, 96, 150, 151].
Tissue Geometry:
Breast ducts and other basement
membrane structures are represented with a signed distance
function d with d > 0 inside the lumen, d = 0 on BM, and d < 0
outside the lumen; d gives the normal vector n to the interior
BM surface. This method is well-suited to describing the
complex BM topology [43].
Molecular dynamics and their effects on cellular
properties: Because the cells’ phenotypic properties (e.g.,
proliferation and apoptosis rates) are not constant throughout
the time course of growth (nor are they uniform across the
tumor[152]), we will investigate the inclusion of a
molecular-scale signaling model into the cell agents. Details are
described in N4.2.D.2.
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Figure 24. A schematic of the agent cell model.
Research Plan Page
Program Director/Principal Investigator (Last, First, Middle):
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Integration Across Scales, and Calibration: We generally use the tissue-scale, continuum model for
overall performance of the simulator, with focused application of the cell-scale model in “patches” where
molecular- and cell-scale dynamics are thought to be important (e.g., in hypoxic regions). To achieve this, we
apply the equation-free approach (EFA) with “gaptooth” dynamics in the selected patches. The EFA, which we
are currently investigating with colleagues in ongoing collaborations across a variety of cancers, accelerates the
discrete code in the patches using coarse time projection/integration methods ([153-157]). The result is a hybrid,
multi-scale tumor simulator which benefits
from the efficiency of the continuum model while
dynamically applying the discrete model where
most needed. We further accelerate for 3D
whole-organ simulations by (i) using the adaptive
multi-grid techniques by our group [39, 44, 47,
48] and (ii) parallelizing the code [158].
Information flows between scales through
dynamic up-scaling and downscaling, and helps
to rigorously integrate data coming from a variety
of spatiotemporal scales (Figure 25). Below, we
give the essential points of the calibration; greater
detail can be found in [41], where we applied the
tissue-scale portion of the calibration to
glioblastoma and [148, 159, 160], where we
applied the cell-scale protocols to ductal
carcinoma in situ of the breast.
A: We initialize the tumor, neo-vasculature by
macroscopic measurements such as MRI or CT.
B: We seed the cell population with the
percentage of cells in the different states, cell
cycle and apoptosis times [148, 159, 160] and
EGFR signaling calibration [96, 150, 151]. The Figure 25. Multi-scale workflow with data flow across biological
scales.
other variables are interpolated from (A). C: The
cell-scale proliferation, apoptosis, and motility parameters are up-scaled to calibrate the continuum scale. D:
update the neo-vasculature, transport, cell densities, and tissue biomechanics. Looping: After completing
(A)-(D), we loop (B)-(D) to simulate the entire lesion and the surrounding microenvironment while efficiently
incorporating the finer spatiotemporal dynamics into the evolution.
The methodology described here can be
easily adopted to study the effects of Notch, Wnt
or Hh inhibitors with the data obtained in Specific
Aims 1.1 and 1.4.
N4.2.D.2 Aim 2: predict the TIC pathways or
key genes related to specific cancer
The bioinformatic analysis of DNA microarray
and proteomic data, coupled with the genetic
and pharmacological manipulations of TIC
function, will enable us to identify key candidate
components in the pathways that are related to
cellular behavior and survival. Subsequently,
we will map these signaling pathway factors to
specific tumor cell types and further to specific
cellular properties by modeling them as
functions of the factors.
N4.2.D.2.1. Modeling of the TIC evolution at
the molecular level
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Input
GENE/Protein
Expression


 t1

...
t
 n
X1
g11
...
gn1
...
...
...
...
Xn 

g1n 

... 
gnn 
PCR;
Microarray.
Output
Cellular
Properties


 t1

...

t
 n
Cellular
Behavior
psy pasy
psy,1 pasy,1
...
...
psy,n pasy,n
Imaging;
Statistics.


1 

... 
n 
Tissue
Properties

CSC

t
CSC
1
1
...
...

tn CSCn
PC TC 

PC1 TC1 
...
... 

PCn TCn 
Markers;
Cytometry.
Figure 26. A schematic representation for the system
integrating information from molecular to cellular and then to
tissue level. The blue parts in the matrices are the data that we
need for validation and that we can get via various techniques
including biological and mathematical methods.
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Cellular behavior is governed, in part, by a complex intracellular signaling network. While models at the
cellular level are incapable of grasping the mechanisms of the decision for cell behavior, they can fit the statistical
results and predict the outcomes of certain conditions. Nevertheless, we also want to incorporate the information
of the lower biological level, namely sub-cellular or molecular level, into our statistical models to further
understand the underlying mechanisms governing cell behavior (Figure 26). Indeed, we have already considered
the effects of micro-environmental factors on the cellular behavior in Aim 2.1 while ignoring some important but
complex networks connecting the environmental factors. Aim 2.2 includes this part to further enrich the proposed
in-silico model.
Several genes, such as BCRA1, HER-2, PTEN, and BMI-1, and many signaling pathways, such as Wnt,
Notch, and Hedgehog, are involved in regulating stem cell behaviors. In breast cancer, activation of these
signaling pathways; amplification of HER-2; or deletion of PTEN may lead to dysregulation of stem-cell
self-renewal, resulting in expansion of the stem cell [82]. Therefore, it is important to integrate the effect of
signaling pathways on the determination of stem cell fate.
There are many publications on the signaling pathways or networks related to stem cells [161-164], most of
which however are described in a conceptual and qualitative manner. Based on these literature and known
biological knowledge, we can construct the mathematical models for pathways from microenvironment factors to
nuclear targets and then to protein expression. However, the signaling pathways are usually complex; for
instance, Hornberg et al. proposed a model of EGFR signaling [165], with 103 chemical species, 148 reactions,
97 independent reaction rates, and 103 initial conditions. Since our aim is to integrate molecular mechanisms
into cellular models, we do not need to deal with the entire signaling pathways in this project. Instead, our
strategy is to identify key factors in the pathways and simplify the extremely complex web of signaling pathways
into a relatively simple but comprehensive mathematical model.
An example of such a strategy is a biomathematical model for studying the effects of HER2 on cell
proliferation by Eladdadi et.al [97]. The model described the interactions between a ligand (EGF) and
corresponding receptors (EGFR and HER-2) by using simple chemical reactions; assuming that the total number
of receptors that can initiate a signal transduction pathway is proportional to the number of cells per unit volume
(i.e. cell density), they modeled the proliferative behavior of cells as a function of HER-2 and EGFR receptors
numbers, and the growth factor EGF as well. Thus, previously complex pathways linking microenvironment
factors to cell fate is greatly simplified. This provides us a certain hint about how to add the signaling pathway into
our initial model established in Aim 2.1 without
exponentially increase in complexity.
As previously described, Wnt, Notch, and
Hedgehog (Hh) signaling pathways are probably
involved in regulation of stem cell fate, we will first
identify key factors in these pathways based on
literature and model analysis as Hornberg et al.
[165] did, i.e. sensitive analysis of each factor, as
well as by bioinformatics methods developed by us.
For example, analysis of DNA microarray data and
protein-protein interactions would enable us to find
the candidate key components in the pathways
which are related to cellular properties, such as the
proliferation rate and patterns of stem cell division
(Figure 4). After that, we map these signaling
pathways to the cellular properties by modeling
them as functions of these factors, thus integrating
simplified pathways models (equations) into the
cellular
model.
Mathematically
psy  f ( x1 ,..., xn ), pasy  g ( x1 ,..., xn ) where x1 ,..., xn are
Figure 27. Integration of signaling pathway(s) into cellular
decision of stem cell fate. Factors from microenvironment,
e.g. EGF, will stimulate signaling pathways targeting nuclear
factors which will in turn induce protein expressions that
modulate cell fate.
genes obtained above, and f and g are the
functions that model the relationship between symmetric or asymmetric rate and the genes in TIC pathways.
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They can be estimated by using x1 ,..., xn and symmetric or asymmetric rate at different time points. Since we
have described in equations (1)-(2) that individual cell populations are functions of psy and pasy , that is,
CSC  f1 ( psy , ...), PC  f 2 ( pasy , ...) , where TIC and PC are cancer stem cells and progenitor cells, we can easily map
x1 ,..., xn into the cellular populations through
psy
and pasy
as follows
: CSC  f1 ( f ( x1 ,..., xn ), ...),
PC  f 2 ( g ( x1 ,..., xn ), ...) .
N4.2.D.2.2. Identify key factors in signaling pathways that modulate TIC fate into the in silico model
As described above, we can evaluate the microarray data of the niche and TIC cells in different stages of
tumor development. Based on these data, and available PPI information, we can infer the signaling pathways
modulating TIC cell fate and progression of MM stem cells.
Network Motif clusters: Although there are many available databases for protein interaction network,
most of them suffer from false-positive data noise. To reduce the negative effects on the findings from network
topology, we filter a high-confidence physical protein network by making sure every interaction is confirmed by at
least two separate databases listed below: IntAct, DIP, MINT, and MIPS [112]. Using this method, filtered protein
network (FPN) composing 2,684 proteins with 3,685 interactions has been identified by us. Network motifs are
patterns that occur in different parts of a network at much higher frequencies than those found in randomized
networks. They are the basic building blocks of all complex networks and do not distribute randomly in the gene
regulatory networks. In the filtered protein network, we also found that its network motifs do not distribute
randomly and display a high clustering property within the network topology.
We defined the pattern or structure of many network motifs (at least two) sharing common proteins as
network motif cluster (NMC). To analyze the contribution of network motif clusters to human cancers, especially
MM, a clustering P-value is proposed to evaluate the extent to which network motif clusters take part in the
signaling pathways of cancers. For a protein in the clustering topology of FPN, we can identify its surrounding
network motifs. Thus, the whole structure of all surrounding network motifs of a protein is called as a motif cluster
(MC), in which the protein shared by network motifs is called as the center proteins (CP) (in red) and every
network motif is called as an arm of the center protein. For different NMCs, the CPs must be distinct from each
other, but some arms of the MCs may be the same network motifs.
Network Inference using diffusion map: Our goal is to identify those high-confidence protein-paths
from the NMCs linking up signaling pathways. Every high-confidence protein-path can be identified by
multiple-objective optimization model for quantifying the
roles of this path in signal transductions specific to TIC.
However, the MOOM could fail when the noise in the
dataset bias the programming into an unreasonable
shortcut. To overcome this problem, we propose a
workflow of defining regulatory network from
multi-modality
data
and
unraveling
signaling
transduction pathway using diffusion map related
method. This method measures gene relationship
focusing more on the connectivity and better reflects the
geometrical structure of whole dataset. We will use
Figure 28. Optimal pathway discovery by diffusion
maps technique. The optimal pathway from A to B in the
diffusion mapping semi-group approach [166] [167] to
mapped large protein interaction network in a spectral
search the shortest path, see Figure 28. Our workflow
view (a) diffusion distance defined from the dataset
starts with constructing a general regulatory network,
structure (b) conventional Euclidean distance.
represented by undirected graph (G, E, W), where G is
the group of interested genes serving as vertices of
graph, E is the group of edges in the graph showing the interaction between different genes and W is the weight
on each edge. The geometrical properties across this graph are then captured using a diffusion map, and
pathway discovery can be done using conventional methods in a mapped space, where the connectivity between
genes are preserved in simple measurement. A Euclidean distance on mapped space actually involved the local
geometry structure around each gene, thus such “diffusion measurement” is robust against outliers in the
dataset.
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N4.2.D.2.3. Modeling from signaling pathways to cell phenotype by using Flux Analysis
As described above, in Aim 1.2, experimental data corresponding to different cell types or various stages of
one cell type can be obtained; in Aim 2.2, bioinformatics analysis of the data enables us to obtain quantitative
expressions of all components (genes or proteins) in signaling pathways (i.e. Wnt, Notch, Hedgehog in this
proposal). Based on the results of these two Specific Aims, we will be able to figure out the interactions networks
in these signaling pathways quantitatively. However, another critical question is: how to infer the cell phenotypes
(cellular behavior parameters) with the information related to signaling pathways?
To this end, flux analysis will be performed on signaling reaction network to calculate flux for each signaling
network. Suppose the network includes n reactants and m reactions. The dynamic mass balance of the
signaling system is described using the flux rates of reactions v  R n1 and time derivatives of reactant
dx
 Sv  0 , where S is
dt
stoichimetric matrix, the system can then be translated into a linear programming problem: min cT v s.t. Sv  0 ,
concentrations x  R m1 . Therefore, at the steady state of the network, i.e., when
v
where c represents the objective function composition, in terms of the fluxes.
Once the programming problem is solved by an optimal solution, the flux distribution v is predicted in the
cell. As a consequence, the relationship between flux distribution and the cell phenotype will be modeled based
on basic linear assumption, e.g. Pi   i v  i for simplification, where Pi are parameters for the cell phenotype,
i and i are constants. Iterative feedback between experiments and simulation results will refine the
mathematical models further.
N4.2.D.3. Aim 2.3: develop image bioinformatics models for discovering critical gene functional
networks from “Directed Iterative Functional Genomic Screen” (DIFGS)
We seek to develop bioinformatics models for discovering gene functional networks by integrating gene function
annotation results from the Directed Iterative Functional Genomics shRNA Screen in Component 1, Aim 1.3 and
publicly available multi-modality genomic data. We will first develop an integrated image analysis system for
shRNA screens, and scoring each gene based on the phenotypic information. We will then develop an imaging
based systems biology approach to study the gene functional networks. Biological processes are often mediated
by an orchestra of genes. Thus gene functional network studies are important to understand gene functions in
detail. Combined with publicly available data, the gene functional annotation results from DIFGS shRNA study
will allow us to identify known/suspected interacting proteins, immediate upstream regulators, and downstream
targets. Compared with the predictions of the refined TIC mE model in Aim 2.2, new experimental data that are
unanticipated by the model can be used to further enhance the robustness of our mathematical TIC mE model.
Image Processing: Each image is segmented into different mammospheres (colonies) where each
mammosphere contains hundreds of cells; and each cell body is delineated in 3D; thus we can extract various
properties for each single cell, including geometry features like length of axis and volume, and intensities of
voxels across cell body described in our preliminary work.
Quantify the effect of shRNA treatment for gene function annotation: We aim to generate a functional
annotation signature for each single gene that is targeted in replicate by different shRNA treatment. To do that
we first record the morphology of each single cell, determine a mammosphere signature to summarize the
information from single cells, and define an image descriptor based on the signature from different
mammospheres in each image. The image descriptors are consolidated across a series of time-lapse images of
a collection of mammospheres from a single shRNA treatment, and are also consolidated across replicate
experiments. The consolidated score summarizes the effect of an shRNA treatment and serve as gene function
signature such that cluster analysis and pathway analysis then can be carried out based on such gene function
signatures.
Colony signature Based on the image processing results, we identify mammospheres in each image and
segmented the 3D image of each cell body. For each cell in a certain mammosphere, we can obtain a series of
region geometry properties G=s(volume, major axis length, eccentricity) For each geometry property g  G , we
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define a vector: Vg  [mg , sd g , max g , min g , rg ] to record the mean value, standard deviation, maximum value,
minimum value and ratio between maxx and minx, respectively. Also, we have a series of intensity properties for
each single cell: Vi  [mi , sdi , max i , cci ] to record the mean, standard deviation, maximum value of intensity
across cell body, and the intensity in the center point of cell body. Combining the number of cells in each colony,
N, with VG and VC, We have a vector C as signature for the properties of a mammosphere: CS  [ N ,VG ,Vi ] .
Image descriptor We summarize the information from all the mammosphere in a single image to form a
vector ID  [ Noc, mN , sd N , max N , min N , mGI , sd GI , Vi I ] . In this image descriptor we include number of
mammospheres in the image Noc ; mean and standard
deviation values, maximum and minimum values for
pre-defined number of cells N in each colony
[mN , sd N , max N , min N ] ; also, we include features in Vi
as well as mean and standard deviation for features in
G while this time all these features are calculated
across the whole image (thus having the superscript I
for mGI , sd GI , Vi I ).
Gene function signature Now that we’ve defined
the ID to describe each image, we have to consolidate
such scores across images from time-lapse imaging
experiments and replicates experiments to form a gene
function signature. Outliers are discarded and
weighted average values are taken based on
correlation coefficients among IDs from replicate
experiments so that repeatable results are obtained.
The obtained mean values are normalized as a Z-score
to control the baseline so that each feature in the
Z-score has similar scale and cluster analysis is
applicable. Also under the time lapse experiment
scenario, we can calculate gene function signature at
each time point and use differential equations to
analyze dynamic of shRNA effect across time.
Network discovery by integrating results of
shRNA screening and functional gene networks:
We propose an integrated workflow to combine single
gene function signature with online genome
Figure 29. Flowchart for quantifying shRNA treatment
information and reconstruct gene regulatory networks
effect for gene function annotation.
related to relevant biological processes. An SVM classifier is utilized to integrate gene function signatures and
heterogeneous genome data to form a functional gene network (FGN). The resulting network is used in
combination with an integer linear programming algorithm [168] to extract a gene regulatory network. Figure 30
shows the flow chart of our proposed method.
In the proposed method as shown in Figure 30, gene function scores are key to measure the relationship among
different genes. Other than that, many different data types can be used to construct a FGN model including PPI
(Protein-protein interaction), gene expression microarray, gene neighborhoods, gene fusion, text mining, and so
on. However, most approaches reported in the literature are based on the integration of PPI and gene
expression microarray data only [169-173]. We integrate physical interaction using PPI downloaded from the
BIND database [111]. This dataset includes 6,772 proteins with 19,372 interactions. Microarray data which are
related cell development are obtained from the GEO database [174]. In the first step PPI data and expression
data are combined to obtain an individual score required for the FGN construction [173]. Several other genomic
datasets with indirect interactions are then recruited to expand FGN, which include gene co-occurrence,
genomic neighborhood, gene fusion, text mining and gene homology data.
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We will utilize SVM (the orange box in Figure 30) to integrate different data sources. The final combined
scores reflect the level of confidence in each individual gene interaction. The input to the SVM consists of the
gene interaction pairs, and we define our true negative gold standard as the collection of pairs that are annotated
in the KEGG but do not occur in the same pathway. An integer linear programming (ILP) based method [168] is
applied to search optimal path with diffusion distance (or signaling pathway), see the previous section. For each
candidate path of a given length, a confidence score is used to evaluate its importance. Given a FGN and
possible starting and end nodes, we can find such a path by maximizing the confidence score and minimizing the
number of edges involved. By predicting a limited set of functionally related candidate genes from RNAi
screening, we can discover pathways not implicated
before. Indeed, several recent papers have
demonstrated how this approach may soon have a
major impact on human disease research.
Therefore, a single integrated network may be
powerfully predictive for many different aspects of
human biology and disease.
Once the target candidate genes and networks
are discovered, we can use the similar approach to
further refine the TIC mE model described in
N4.2.D.2.4. This refined TIC mE model will be used
to predict the response of TIC with genetic and
pharmacological treatment.
N4.2.D.4. Aim 2.4: model the response of TIC and
their microenvironment according to genetic
and pharmacological manipulations of TIC
function
Figure 30. Flow chart for FGN discovery. The blue boxes
present the heterogeneous data sources; different types are
used as inputs to a linear SVM classifier, which outputs a
combined score. The orange box denotes the obtained
weighted functional gene network. The red box denotes our
final result, the extracted MAPK signaling pathway.
For the genetic manipulations, cell cultures and a
novel collection of mouse tumors and low passage
human xenografts will be used to study the effects of
genetic and pharmacological TIC inhibitors on tumor cell behavior in vitro and on tumor development in vivo.
Based on these data and bioinformatics technologies, especially the signaling pathway modeling methods as
described previously in the molecular level, we will first find the related signaling pathways and then describe the
effects of these inhibitors on cellular behavior with mathematical equations (e.g., by virtually downregulating the
corresponding portion of the network model), which will then be automatically integrated into the tumor growth
model via the multiscale framework, specifically through the upscaling of the molecular/cellular agent model in
the continuum model. In this way, the genetic manipulations can be incorporated into the mathematical model.
By changing parameter values in the model corresponding to effects of genetic TIC inhibitors, we can simulate
the outcomes for validation and prediction, without doing many more biological experiments.
For the pharmacological manipulations, we model the drug delivery from the vasculature through the whole
microenvironment to the tumor site, and further transport from the extracelluar compartment into the intracellular
compartment. In addition, the cytotoxic function will be integrated. This technique has been employed in our
multiscale modeling framework
in [42, 46].
S 
S 
λd ,U,V ρ V d  k 12 d  k 21  2   k 41  4 
Pharmacokinetics
(PK)
 VC 
 VC 
model:
We
model
the

S 
dS 2
extravasation of drug from the
 k 12VC d  k 21S 2  k 23 S 2 1  3   k 32 S 3  k 24 S 2  k 42 S 4
vasculature
and
diffusion
dt
 Vm 
(16)
through the tissue using the

dS 3
S 
reaction-diffusion
equations
 k 12 S 2 1  3   k 32 S 3
dt
 Sm 
(11) previously introduced in
Section D.4.2.D.1.2, with slight
dS 4
 k 24 S 2  k 42 S 4  k 41S 4
modifications for drug pumping
dt
as we now describe. The local
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transport of drugs from the interstitium into the cells’ cytoplasm and nucleoli is described using a compartmental
model at each computational grid point as in [42, 46]. The model consists of four compartments, namely, the
local extracellular drug concentration d, the cytosolic concentration S2, the nucleolar DNA_bound concentration
S3, and the lysosomal drug content S4, as described in System (16) and in [42, 46, 49]. In particular, note that the
first equation in System (16) is the net drug delivery rate for the reaction-diffusion drug transport in Equation (11).
Also, kij is a transfer rate from compartment i to j (S1 is identical to d), ki is a rate of removal from compartment i,
Sm is a DNA saturation parameter, and Vc is the volume of a cell. All these parameters are estimated for
doxorubicin in breast cancer cells in [42, 46]. This system may be modified according to the mechanism of the
therapeutic agent. For example, erlotinib is a TKI that disrupts EGFR autophosphorylation by binding at the
intracellular ATP sites just below the cell membrane, and hence the nucleolar and lysosomal components are
unnecessary for describing its pharmacodynamics. Note that these equations are just an example for the
modeling of drug delivery in the microenvironment and intracellular space, and compartments in this system can
be added or removed due to different biological characteristics of drugs.
Pharmacodynamic (PD) model: This model describes the mechanism of the drugs cytotoxicity, and can be
implemented with various levels of detail. For chemotherapeutic compounds such as doxorubicin that act by
inducing apoptosis in cycling cells, we implement a phenomenological model as in [46, 49, 87, 175]. This avoids
the issues of unknown drug mechanisms and instead focuses upon the quantitative effects of the drugs. A
typical phenomenological PD model for cytotoxic agents that rely upon binding to DNA (e.g., doxorubicin) is
given by the Hill-type equation (17), where E is cell inhibition, σ is the nutrient level, x is the DNA-bound drug time
E  N   / 1  A1 x  m  , where x  tˆ    S4  s  ds,
tˆ
0
(17)
(calculated in each computational grid point as a function of elapsed time tˆ since the administration of the
compound), and A and m are fitted parameters. N(σ) is used to model the impact of the nutrient-dependence of
drug effectiveness, which stems from the fact that such cytotoxic agents act primarily upon cycling cells; more
information is given in [42, 46]. In discrete models, we can use x to alter the non-quiescent cells’ probability of
entering the apoptotic state similarly to the hypoxic model in [43]. For the action of erlotinib upon EGFR
pathway-addicted cells, we can implement PD either by setting the EGFR signaling rates to 0 in the detailed
EGFR pathway models or by varying the rate of apoptosis with both the concentration of drug d and the rate of
EGFR:EGF binding. In the latter case, EGFR:EGF is presumed to not transmit its signal for cell survival, and so
the apoptosis rate is increased.
N4.3. Other Items
Optional Element - Shared Resources Cores. We will not propose a new supporting core, but make use of
the existing cores. In Section N.2.5, we described the Supporting Cores. Pilot Research Efforts. Each
awarded CCSB will be expected to pursue new opportunities pertinent to the scientific theme of the Center. We
have described the strategies in Section N.2.4: Management of Pilot projects.
Section N5: Component 3 - Education, Training, and Outreach Program
N5.1. Rationale for an Educational & Training Program in Systematic Modeling of Cancer Development
Breast cancer is the most commonly diagnosed cancer, and the second leading cause of cancer deaths
among American women. As such, breast cancer has been identified as a public health priority in the United
States. Despite this clinical and social importance, we are only now beginning to understand the molecular,
cellular, and developmental mechanisms underlying breast cancer initiation and progression in enough detail to
allow rudimentary predictions of treatment response to be made. Mathematical modeling and computational
simulation offers the extraordinary promise of integrating huge quantities of diverse data into coherent
developmental and predictive models capable of informing “personalized medicine” decisions. However, these
models can only be as good as our biological understanding of the parameters involved in breast cancer
development, and as the experimental data on which they are based.
Our educational and training plan is designed to fill a need for an organized training process in
combined biological and mathematical/computational modeling of breast cancer for postdoctoral
fellows and undergraduates. In subsequent funding cycles, we intend to extend this training program to
include graduate student trainees.
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Our proposed training program brings together, in a formal way, researchers from clinical, translational, and
basic science areas of experimental breast cancer research, as well as researchers in the areas of mathematical
modeling, computational biology and bioinformatics with a keen interest in working toward a common goal of
understanding breast cancer biology. In this proposal, we will establish two unique multidisciplinary training
programs, one for undergraduates and one for postdoctoral trainees.
The undergraduate program will be entitled the “Multidisciplinary Summer Undergraduate Training Program
in Experimental and Mathematical Modeling of Cancer.” This program will be geared toward individuals exploring
their interest in cancer research and will serve to introduce undergraduates to various aspects of experimental
biology and to mathematical/computational modeling approaches and analyses.
The multidisciplinary postdoctoral training program will be geared toward recruitment and training of
individuals holding Ph.D. or M.D./Ph.D. degrees in mathematical modeling, computational biology,
biostatistics/bioinformatics, or a related advanced degree who are interested in gaining significant experimental
experience and deeper conceptual insight into breast cancer development in a laboratory or clinical setting.
Prospective candidates will be teamed both with a basic science/clinical mentor, and a mathematical
modeling/computational biology mentor, for the development of a suitable research project addressing an
important unanswered question in breast cancer biology from a combined experimental and
mathematical/computational perspective.
N5.1.1 Summer Undergraduate Training Program in Experimental and Mathematical Modeling of Cancer
A summer undergraduate student research program has been operated in The Methodist Hospital Research
Institute for 5 consecutive years. The program is a 10 weeks program. The stipend for the 10 weeks is $5,000.00.
The students are placed in a research laboratory with a designated mentor where they are assigned a specific
project. During this time they also have to attend weekly didactic lectures given by the leading researchers and
physicians in The Methodist Hospital Research Institute and Methodist Hospital. At the end of the program there
is a Student Retreat where the students present their work to the group of students and faculty mentors. Each
year 20-25 students are admitted to the program. The Institute provides overall administrative support, but
mentors are responsible for the summer stipend through their research funding.
We will build upon this established infrastructure using funds from this ICBP program to fund up to five
additional undergraduate students. These five undergraduate students can be distributed to individual
laboratories across three institutions, TMHRI, Baylor, and UTHSC associated with the CSMCaD and assigned a
specific cancer research project comprised of both an experimental and a mathematical/computational
component with both a wet lab and dry lab mentoring. The laboratory topics studied and techniques used will
vary considerably from lab to lab, however the training may include in-vitro and in-vivo imaging, preparation and
analysis of genomics, preparation and analysis of protein and antibody arrays, reporter technology, biological
image analysis, computational modeling of tumor progression and drug response.
N5.1.2. Postdoctoral Training Program in Experimental and Mathematical Modeling of Cancer.
In this joint, multidisciplinary program, our major objective will be the training of bright and ambitious
postdoctoral fellows holding PhD degrees in fields such as Mathematics, Computer Science, or Biological
Engineering to become well-versed laboratory or clinical researchers with a deep intellectual understanding of
breast cancer initiation and progression. In essence, we seek to develop a group of independent researchers
who can “speak at least two languages” fluently. This effort, in turn, will enhance the quality and breadth of
research in the participating institutions.
We will provide annual salaries commensurate with experience according to the NIH-NRSA guidelines. 2
Postdoctoral fellows will be budgeted with 0 years of experience ($37,368/year), and 1 fellow will be budgeted
with 3 years of experience ($43,860/year). In addition to salary, we will support travel to attend one scientific
meeting per year. We will advertise the postdoc positions nationwide.
N5.1.3. Each trainee will have two mentors – a basic/clinical science mentor and a
mathematical/computational modeling mentor.
An internationally recognized breast cancer research program has existed for many years in the group that now
constitutes the Lester and Sue Smith Breast Center at Baylor College of Medicine, and this program has offered
a unique opportunity for postdoctoral fellows interested in starting careers in translational breast cancer
research. As with the undergraduate summer program, we will build upon this established training infrastructure
with the addition of faculty from an internationally recognized mathematical modeling/computational biology
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programs at TMHRI and other institutions to build a multidisciplinary postdoctoral training program in
experimental and mathematical modeling of breast cancer.
In addition to numerous individual research grants from federal, private, and industry sources, breast
program faculty have an active, well-supported, and multi-disciplinary research program in breast cancer
including a P01 program project grant (Steffi Oesterreich Ph. D., PI), a Breast Cancer SPORE grant (C. Kent
Osborne, M.D., PI), a Susan G. Komen Promise grant (Powel Brown, MD. Ph.D., PI) for the study of triple
negative breast cancer, as well as an AACR “Stand up to cancer” multi-institutional grant to study mechanisms of
treatment resistance (C. Kent Osborne, PI). Finally, we have been awarded a Cancer Center grant from the NCI
(C. Kent Osborne, M.D., PI), which will further facilitate and strengthen breast cancer research initiatives at
Baylor and associated institutions.
Many members of TMHRI’s Bioinformatics and Biomedical Engineering Program come from the HCNR
Center for Bioinformatics at Harvard Medical School and Functional and Molecular Imaging Center at Brigham
and Women’s Hospital. They have different kinds of funding sources from NIH, NSF, DOD, and foundation
grants. Four NIH R01 grants are ongoing in the area of Image bioinformatics and Computational Biology.
N5.2. Program Administration
The Core PI (Director) for this Educational and Training Program will be Dr. Michael T. Lewis, Ph.D. (BCM). Dr.
Lewis is an Assistant Professor of Molecular and Cellular Biology, and a faculty member of the Lester and Sue
Smith Breast Center at BCM. In addition to extensive didactic teaching experience at the graduate and
undergraduate levels, Dr. Lewis has trained 3 postdoctoral fellows, and serves as a basic science mentor for two
clinical fellows. In addition, Dr. Lewis currently has three graduate students in his laboratory. Dr. Lewis has a
strong commitment to training at every level and will be mentored by Drs. Fuqua and Wong in direction of the
training program. Dr. Lewis will spend 5% of his time directing this program.
The co-directors for the Educational and Training Program will be Drs. Suzanne A. W. Fuqua, Ph.D., (BCM)
and Stephen Wong, Ph.D. (TMH).
Dr. Fuqua has been directly responsible for the administration of a T32 training Program in Breast Cancer
for many years. Dr. Fuqua is a Professor of Medicine, and a senior faculty member of the Lester and Sue Smith
Breast Center at BCM. She has trained 29 pre- and postdoctoral fellows, including both M.D. and Ph.D. trainees,
and is currently training three predoctoral graduate students in the Molecular and Cell Biology and the
Translational Biology and Molecular Medicine (TBMM) graduate programs at Baylor. Dr. Fuqua will spend 5% of
her time administering this Program and will serve as an on-site mentor to Dr. Lewis.
Dr. Wong is currently the faculty mentor for the TMHRI undergraduate program, as well as for the Methodist
Hospital's Departments of Radiology and Pathology residents and postdoctoral fellow program. In addition he
serves as a mentor in the BCM computational and structural biology and biophysics graduate program, the
departments of bioengineering at Rice and University of Houston programs, and the department of mechanical
engineering program at the University of Houston, both undergraduate and graduate levels, as well as school of
health information sciences, UTHSC. Dr. Wong also participated in several T32 training programs in biomedical
informatics, bioengineering, and genetics at Harvard and UCSF for about fifteen years. Dr. Wong has trained 14
PhDs and 35 postdoctoral fellows. Dr. Wong will spend 5% of his time administering this Program.
N5.3. Administrative Structure
The Training Program in Systematic Modeling of Cancer Development is a 2 to 3 year program. Drs.
Lewis, Fuqua, and Wong will constitute an Executive Steering Committee, which will orchestrate the selection of
research preceptors by the trainees, assuring that the trainees meet the eligibility criteria and are paired with the
appropriate wet lab and dry lab mentors. We will make use of prepared payback agreements, activation notices,
and progress reports, and to manage the financial aspects of the Program. The Executive Steering Committee
will organize the didactic components, and will spearhead the integration of two areas of training required by
candidates. We will also monitor the progress of all trainees in terms of their didactic experience and their
research progress through weekly ”Research in Progress” presentations, and monthly Journal Club. In addition,
the Executive Steering Committee will meet two or three times per year specifically to discuss the final selection
of research preceptors, the progress of each trainee, and the content of the didactic components of the training
program.
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N.5.3.1. Program Faculty – Mathematical Modeling and Computational Biology Co-Mentors
Vittorio Cristini, Ph.D. Associate Professor, UT Health Science Center at Houston; Susan Hilsenbeck,
Ph.D. Professor, Baylor College of Medicine (BCM); Chad Shaw Ph.D. Assistant Professor of Genetics at the
Baylor College of Medicine; Stephen TC Wong, Ph.D. John S Dunn Distinguished Endowed Chair in Biomedical
Engineering; Professor in Radiology, Weill Cornell Medical College; Xiaobo Zhou, Ph.D. Associate Professor of
Bioinformatics in Radiology, WCMC; Fei Cao, Ph.D. Assistant Professor of Radiology, WCMC, will sever as a
memeter. The profiles of these professors can be found at Section N1.4. We also have Zhong Xue, Ph.D.,
Assistant Professor of Electrical Engineering in Radiology, WCMC, and Chief of Medical Image Analysis Lab,
TMHRI-BBE Program; and Kelvin Wong, Ph.D., Assistant Professor of Electronic Engineering in Radiolgoy,
WCMC, and Chief of Translational Multimodality Optical Imaging Lab, TMHRI-BBE Program, will serve mentors.
N5.3.2 Program Faculty – Basic/Clinical Science Co-Mentors
Jenny Chang, M.D., Professor of Medicine, BCM; Mary Dickinson, Ph.D., Associate Professor of
Molecular Physiology and Biophysics, BCM; Dean Edwards, Ph.D., Professor of Molecular and Cellular
Biology, BCM; Suzanne A.W. Fuqua, Ph.D., Professor of Medicine, BCM; Michael T. Lewis, Ph.D., Assistant
Professor of Molecular and Cellular Biology, BCM. Jeffrey M. Rosen, Ph.D., C.C. Bell Professor of Molecular
and Cellular Biology, BCM, will serve as a mentor. Their profiles can be found at Section N1.4.
Other professors include: Powel H. Brown, M.D., Ph.D., Professor of Medicine, BCM, an expert in
understanding the process of breast carcinogenesis and on developing more effective ways to prevent breast
cancer; Eric Chang, Ph.D., Associate professor of Molecular and Cellular Biology, BCM, expert in studying the
Ras GTPases; Yi Li, Ph.D., Assistant Professor of Molecular and Cellular Biology, BCM, an expert in dissecting
the molecular interactions in breast carcinogenesis by studying how the Wnt and other oncogenic pathways
interact to induce mammary tumors using mouse models; Daniel Medina, Ph.D., Professor of Molecular and
cellular Biology, BCM, an expert in the study of early breast tumor development and prevention; Bert O’Malley,
M.D., Professor and Chair of Molecular and Cellular Biology, BCM, an expert in studying "primary molecular
endocrine pathway"; C. Kent Osborne, M.D., Professor of Medicine, Director of the Dan L. Duncan Cancer
Center and the Lester and Sue Smith Breast Center, Chair of the Department of Hematology and Oncology,
BCM, an expert in identifying molecular mechanisms by which breast cancer cells become resistant to the
antiestrogen tamoxifen; and Nancy Weigel, Ph.D. Professor of Molecular and Cellular Biology, BCM, an expert
in studying how cell signaling influences PR function.
N5.3.3 Didactic Components of the Training Program for Postdoctoral Fellows
TMHRI provides additional training in Mathematics and Computational Biology. It includes bioinformatics, image
bioinformatics, systems biology, biomedical imaging informatics, molecular imaging, biostatistics, and
biomathmatics. One area of TMHRI computational biology effort focuses on mathematical modeling and
computer simulations to study various aspects of tumor initiation, progression, and treatment, model
development and its integration with experimental data, clinical data, or both data: bio-mechanics of normal vs.
tumor-like tissue morphogenesis, micro-fluids in drug delivery, biophysics of tumor microenvironment. The
courses also include mathematical, computational or engineering sciences; computational fluid dynamics,
computer programming and visualization.
The Graduate School at Baylor College of Medicine provides a variety of basic science courses that lead to the
award of a Ph.D. degree. Courses taught in Molecular and Cellular Biology, Biochemistry, or Molecular and
Human Genetics may be appropriate for individual trainees. At the discretion of the preceptor, trainees will audit
these courses in order to broaden their basic science background.
There are two courses that are currently offered by the Graduate School which are required for trainees in the
Program: “Cancer Core Curriculum Course” and “Introduction to Molecular Carcinogenesis.” The “Cancer”
course covers cancer as a multi-step process, initiation of carcinogenesis, progression of carcinogenesis,
oncogenes, and tumor suppressor genes. For training in breast cancer research Postdoctoral trainees will be
required to take three additional courses (listed below). Additional courses will be recommended on an individual
basis as needs arise.
1. Translational Breast Cancer Research Course – Dr. Fuqua
Because many of our trainees are expected come to the Training Program with little if any knowledge of breast
cancer, we have designed this lecture series to familiarize them with breast cancer from the standpoint of
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clinicians and translational basic scientists. The course objective is to provide a broad understanding of current
problems in breast cancer, experimental approaches, and active research areas in the field.
2. Introduction to Biostatistics Course for Translational Researchers – Dr. Hilsenbeck
While some candidates will have a strong background in Biostatistics, some may not and will benefit from a basic
biostatistics course. It is our experience that few trainees in laboratory investigation receive any organized
instruction in research design or statistical evaluation, and when such training exists, design and statistical
methodologies appropriate for the special problems of clinically-oriented laboratory research are virtually never
included. Dr. Hilsenbeck is the principal designer and instructor of this course.
3. Scientific Writing and Research Grants Course — Dr. Gary C. Chamness
This course focuses on writing more readable (and fundable) research grants, including specific writing skills,
construction of grant elements, layout and other technical aspects, and some points on strategy and tactics.
Construction of scientific papers is also covered. We combine lecture and interactive formats, and there are also
short but important homework exercises. Dr. Chamness has been an NIH study section member and editor of
two journals as well as having published over 100 papers and written numerous grant proposals both large and
small, so that he brings both the reader's and the writer's perspective to this course.
In addition, UTHSC also offers formal courses in computational biology and bioinformatics.
Moreover, trainees must participate in: (1) Breast Disease Research in Progress Seminars: Trainees are
required to attend a weekly Breast Disease Research in Progress Seminars program (Directed by Dr. Lewis); (2)
Mathematical Biology and Breast Cancer Journal Clubs: Trainees also attend a Breast Cancer Journal Club,
held twice a month at BCM and a Mathematical Biology and Bioinformatics Journal Club, also held bi-weekly at
TMHRI; and (3) Educational Seminars: Trainees also attend three regularly scheduled seminar series. The
first is a Breast Disease Workshop organized by Dr. Medina and Dr. Li. This weekly seminar series is given by
members of the Baylor faculty from any of several departments who are interested in basic, translational, or
clinical research in breast cancer. The second seminar series is a monthly outside Distinguished Speaker
program co-sponsored by the Breast Center, the Cancer Center, and the Department of Molecular and Cellular
Biology. The third is a bi-weekly bioinformatics and imaging research seminar series organized by the PI at The
Methodist Hospital, which features research topics in computational biology, systems biology, bioinformatics,
and broad spectrum of microscopy imaging and medical imaging as well as their applications in clinical research
and disease management. In addition, they are encouraged to select other seminars of interest from the dozens
offered at Baylor, Methodist, and UTHSC each week to supplement their learning experience.
N5.3.4 Recruitment Plan for Trainee Candidates
Postdoctoral trainee candidates have been chosen from among applicants who have completed their Ph.D. in
mathematics, computational biology, bioengineering, biostatistics, biophysics, and who wish to pursue fellowship
training in breast cancer or other related cancer research. For active recruitment of postdoctoral trainee
candidates we will use several approaches. 1) We will advertise in international scientific journals including
Science, Nature, Cell, and Bioinformatics. Such ads typically garner about 400 applications. 2) We will seek
candidates through national meetings of relevant societies including the American Association for Cancer
Research, the American Society of Clinical Oncology, the San Antonio Breast Cancer Symposium, and the
Endocrine Society. This has been a valuable source of candidates and many trainees in the Breast Center
supported by other funds have been recruited via the San Antonio meeting. We will also advertise through
bioinformatics and computational biology meetings to recruit mathematical/computational candidates, including
meetings of International Society of Computational Biology, Biophysical Society, and IEEE (Institute of Electrical
and Electronic Engineers). 3) We will send program announcements to relevant departments of other
universities and institutions, backed up by active recruiting during faculty visits to these institutions. 4) We will
pursue recruiting contacts at local colleges and universities, including the University of Houston, Rice University,
Texas A&M, University of Texas at Austin, and Texas Southern University. 5) We will also set up recruitment
website at CSMCaD web portal and TMHRI web portal for announcing the opening positions and accepting
applications on line. A similar approach will be used to recruit summer undergraduate students, and graduate
students in later years of the center, after postdocs and summer student programs are operational.
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